Uncommon Descent Serving The Intelligent Design Community

The Ubiquitin System: Functional Complexity and Semiosis joined together.

Share
Facebook
Twitter
LinkedIn
Flipboard
Print
Email

This is a very complex subject, so as usual I will try to stick to the essentials to make things as clear as possible, while details can be dealt with in the discussion.

It is difficult to define exactly the role of the Ubiquitin System. It is usually considered mainly a pathway which regulates protein degradation, but in reality its functions are much wider than that.

In essence, the US is a complex biological system which targets many different types of proteins for different final fates.

The most common “fate” is degradation of the protein. In that sense, the Ubiquitin System works together with another extremely complex cellular system, the proteasome. In brief, the Ubiquitin System “marks” proteins for degradation, and the proteasome degrades them.

It seems simple. It is not.

Ubiquitination is essentially one of many Post-Translational modifications (PTMs): modifications of proteins after their synthesis by the ribosome (translation). But, while most PTMs use simpler biochemical groups that are usually added to the target protein (for example, acetylation), in ubiquitination a whole protein (ubiquitin) is used as a modifier of the target protein.

 

The tool: Ubiquitin

Ubiquitin is a small protein (76 AAs). Its name derives from the simple fact that it  is found in most tissues of eukaryotic organisms.

Here is its aminoacid sequence:

MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPD

QQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG

Essentially, it has two important properties:

  1. As said, it is ubiquitous in eukaryotes
  2. It is also extremely conserved in eukaryotes

In mammals, ubiquitin is not present as a single gene. It is encoded by 4 different genes: UBB, a poliubiquitin (3 Ub sequences); UBC, a poliubiquitin (9 Ub sequences); UBA52, a mixed gene (1   Ub sequence + the ribosomal protein L40); and RPS27A, again a mixed gene (1 Ub sequence + the ribosomal protein S27A). However, the basic ubiquitin sequence is always the same in all those genes.

Its conservation is one of the highest in eukaryotes. The human sequence shows, in single celled eukaryotes:

Naegleria: 96% conservation;  Alveolata: 100% conservation;  Cellular slime molds: 99% conservation; Green algae: 100% conservation; Fungi: best hit 100% conservation (96% in yeast).

Ubiquitin and Ubiquitin like proteins (see later) are characterized by a special fold, called  β-grasp fold.

 

The semiosis: the ubiquitin code

The title of this OP makes explicit reference to semiosis. Let’s try to see why.

The simplest way to say it is: ubiquitin is a tag. The addition of ubiquitin to a substrate protein marks that protein for specific fates, the most common being degradation by the proteasome.

But not only that. See, for example, the following review:

Nonproteolytic Functions of Ubiquitin in Cell Signaling

Abstract:

The small protein ubiquitin is a central regulator of a cell’s life and death. Ubiquitin is best known for targeting protein destruction by the 26S proteasome. In the past few years, however, nonproteolytic functions of ubiquitin have been uncovered at a rapid pace. These functions include membrane trafficking, protein kinase activation, DNA repair, and chromatin dynamics. A common mechanism underlying these functions is that ubiquitin, or polyubiquitin chains, serves as a signal to recruit proteins harboring ubiquitin-binding domains, thereby bringing together ubiquitinated proteins and ubiquitin receptors to execute specific biological functions. Recent advances in understanding ubiquitination in protein kinase activation and DNA repair are discussed to illustrate the nonproteolytic functions of ubiquitin in cell signaling.

Another important aspect is that ubiquitin is not one tag, but rather a collection of different tags. IOWs, a tag based code.

See, for example, here:

The Ubiquitin Code in the Ubiquitin-Proteasome System and Autophagy

(Paywall).

Abstract:

The conjugation of the 76 amino acid protein ubiquitin to other proteins can alter the metabolic stability or non-proteolytic functions of the substrate. Once attached to a substrate (monoubiquitination), ubiquitin can itself be ubiquitinated on any of its seven lysine (Lys) residues or its N-terminal methionine (Met1). A single ubiquitin polymer may contain mixed linkages and/or two or more branches. In addition, ubiquitin can be conjugated with ubiquitin-like modifiers such as SUMO or small molecules such as phosphate. The diverse ways to assemble ubiquitin chains provide countless means to modulate biological processes. We overview here the complexity of the ubiquitin code, with an emphasis on the emerging role of linkage-specific degradation signals (degrons) in the ubiquitin-proteasome system (UPS) and the autophagy-lysosome system (hereafter autophagy).

A good review of the basics of the ubiquitin code can be found here:

The Ubiquitin Code 

(Paywall)

It is particularly relevant, from an ID point of view, to quote the starting paragraph of that paper:

When in 1532 Spanish conquistadores set foot on the Inca Empire, they found a highly organized society that did not utilize a system of writing. Instead, the Incas recorded tax payments or mythology with quipus, devices in which pieces of thread were connected through specific knots. Although the quipus have not been fully deciphered, it is thought that the knots between threads encode most of the quipus’ content. Intriguingly, cells use a regulatory mechanism—ubiquitylation—that is reminiscent of quipus: During this reaction, proteins are modified with polymeric chains in which the linkage between ubiquitin molecules encodes information about the substrate’s fate in the cell.

Now, ubiquitin is usually linked to the target protein in chains. The first ubiquitin molecule is covalently bound through its C-terminal carboxylate group to a particular lysine, cysteine, serine, threonine or N-terminus of the target protein.

Then, additional ubiquitins are added to form a chain, and the C-terminus of the new ubiquitin is linked to one of seven lysine residues or the first methionine residue on the previously added ubiquitin.

IOWs, each ubiquitin molecule has seven lysine residues:

K6, K11, K27, K29, K33, K48, K63

And one N terminal methionine residue:

M1

And a new ubiquitin molecule can be added at each of those 8 sites in the previous ubiquitin molecule. IOWs, those 8 sites in the molecule are configurable switches that can be used to build ubiquitin chains.

Her are the 8 sites, in red, in the ubiquitin molecule:

MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPD

QQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG

Fig 1 shows two ubiquitin molecules joined at K48.

Fig 1 A cartoon representation of a lysine 48-linked diubiquitin molecule. The two ubiquitin chains are shown as green cartoons with each chain labelled. The components of the linkage are indicated and shown as orange sticks. By Rogerdodd (Own work) [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

The simplest type of chain is homogeneous (IOWs, ubiquitins are linked always at the same site). But many types of mixed and branched chains can also be found.

Let’s start with the most common situation: a poli-ubiquitination of (at least) 4 ubiqutins, linearly linked at K48. This is the common signal for proteasome degradation.

By the way, the 26S proteasome is another molecular machine of incredible complexity, made of more than 30 different proteins. However, its structure and function are not the object of this OP, and therefore I will not deal with them here.

The ubiquitin code is not completely understood, at present, but a few aspects have been well elucidated. Table 1 sums up the most important and well known modes:

Code

Meaning

Polyubiquitination (4 or more) with links at K48 or at K11 Proteasomal degradation
Monoubiqutination (single or multiple) Protein interactions, membrane trafficking, endocytosis
Polyubiquitination with links at K63 Endocytic trafficking, inflammation, translation, DNA repair.
Polyubiquitination with links at K63 (other links) Autophagic degradation of protein substrates
Polyubiquitination with links at K27, K29, K33 Non proteolytic processes
Rarer chain types (K6, K11) Under investigation

 

However, this is only a very partial approach. A recent bioinformatics paper:

An Interaction Landscape of Ubiquitin Signaling

(Paywall)

Has attempted for the first time a systematic approach to deciphering the whole code, using synthetic diubiquitins (all 8 possible variants) to identify the different interactors with those signals, and they identified, with two different methodologies,  111 and 53 selective interactors for linear polyUb chains, respectively. 46 of those interactors were identified by both methodologies.

The translation

But what “translates” the complex ubiquitin code, allowing ubiquinated proteins to met the right specific destiny? Again, we can refer to the diubiquitin paper quoted above.

How do cells decode this ubiquitin code into proper cellular responses? Recent studies have indicated that members of a protein family, ubiquitin-binding proteins (UBPs), mediate the recognition of ubiquitinated substrates. UBPs contain at least one of 20 ubiquitin-binding domains (UBDs) functioning as a signal adaptor to transmit the signal from ubiquitinated substrates to downstream effectors

But what are those “interactors” identified by the paper (at least 46 of them)? They are, indeed, complex proteins which recognize specific configurations of the “tag” (the ubiquitin chain), and link the tagged (ubiquinated) protein to other effector proteins which implement its final fate, or anyway contribute in deffrent forms to that final outcome.

 

The basic control of the procedure: the complexity of the ubiquitination process.

So, we have seen that ubiquitin chains work as tags, and that their coded signals are translated by specific interactors, so that the target protein may be linked to its final destiny, or contribute to the desired outcome. But we must still address one question: how is the ubiquitination of the different target proteins implemented? IOWs, what is the procedure that “writes” the specific codes associated to specific target proteins?

This is indeed the first step in the whole process. But it is also the most complex, and that’s why I have left it for the final part of the discussion.

Indeed, the ubiquitination process needs to realize the following aims:

  1. Identify the specific protein to be ubiquitinated
  2. Recognize the specific context in which that protein needs to be ubiquitinated
  3. Mark the target protein with the correct tag for the required fate or outcome

We have already seen that the ubiquitin system is involved in practically all different cellular paths and activities, and therefore we can expect that the implementation of the above functions must be a very complex thing.

And it is.

Now, we can certainly imagine that there are many different layers of regulation that may contribute to the general control of the procedure, specifically epigenetic levels, which are at present poorly understood. But there is one level that we can more easily explore and understand, and it is , as usual, the functional complexity of the proteins involved.

And, even at a first gross analysis, it is really easy to see that the functional complexity implied by this process is mind blowing.

Why? It is more than enough to consider the huge number of different proteins involved. Let’s see.

The ubiquitination process is well studied. It can be divided into three phases, each of which is implemented by a different kind of protein. The three steps, and the three kinds of proteins that implement them, take the name of E1, E2 and E3.

 

Fig. 2 Schematic diagram of the ubiquitylation system. Created by Roger B. Dodd: Rogerdodd at the English language Wikipedia [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0/)], via Wikimedia Commons

 The E1 step of ubiquitination.

This is the first thing that happens, and it is also the simplest.

E1 is the process of activation of ubiquitin, and the E1 proteins is called E1 ubiquitin-activating enzyme. To put it simply, this enzyme “activates” the ubiquitin molecule in an ATP dependent process, preparing it for the following phases and attaching it to its active site cysteine residue. It is not really so simple, but for our purposes that can be enough.

This is a rather straightforward enzymatic reaction. In humans there are essentially two forms of E1 enzymes, UBA1 and UBA6, each of them about 1000 AAs long, and partially related at sequence level (42%).

 

The E2 step of ubiquitination.

The second step is ubiquitin conjugation. The activated ubiquitin is transferred from the E1 enzyme to the ubiquitin-conjugating enzyme, or E2 enzyme, where it is attached to a cysteine residue.

This apparently simple “transfer” is indeed a complex intermediate phase. Humans have about 40 different E2 molecules. The following paper:

E2 enzymes: more than just middle men

details some of the functional complexity existing at this level.

Abstract:

Ubiquitin-conjugating enzymes (E2s) are the central players in the trio of enzymes responsible for the attachment of ubiquitin (Ub) to cellular proteins. Humans have ∼40 E2s that are involved in the transfer of Ub or Ub-like (Ubl) proteins (e.g., SUMO and NEDD8). Although the majority of E2s are only twice the size of Ub, this remarkable family of enzymes performs a variety of functional roles. In this review, we summarize common functional and structural features that define unifying themes among E2s and highlight emerging concepts in the mechanism and regulation of E2s.

However, I will not go into details about these aspects, because we have better things to do: we still have to discuss the E3 phase!

 

The E3 step of ubiquitination.

This is the last phase of ubiquitination, where the ubiquitin tag is finally transferred to the target protein, as initial mono-ubiquitination, or to build an ubiquitin chain by following ubiqutination events. The proteins which implement this final passage are call E3 ubiquitin ligases. Here is the definition from Wikipedia:

A ubiquitin ligase (also called an E3 ubiquitin ligase) is a protein that recruits an E2 ubiquitin-conjugating enzyme that has been loaded with ubiquitin, recognizes a protein substrate, and assists or directly catalyzes the transfer of ubiquitin from the E2 to the protein substrate.

It is rather obvious that the role of the E3 protein is very important and delicate. Indeed it:

  1. Recognizes and links the E2-ubiquitin complex
  2. Recognizes and links some specific target protein
  3. Builds the appropriate tag for that protein (Monoubiquitination, mulptiple monoubiquitination, or poliubiquitination with the appropriate type of ubiquitin chain).
  4. And it does all those things at the right moment, in the right context, and for the right protein.

IOWs, the E3 protein writes the coded tag. It is, by all means, the central actor in our complex story.

So, here comes the really important point: how many different E3 ubiquitin ligases do we find in eukaryotic organisms? And the simple answer is: quite a lot!

Humans are supposed to have more than 600 different E3 ubiquitin ligases!

So, the human machinery for ubiquitination is about:

2 E1 proteins  –  40 E2 proteins – >600 E3 proteins

A real cascade of complexity!

OK, but even if we look at single celled eukaryotes we can already find an amazing level of complexity. In yeast, for example, we have:

1 or 2 E1 proteins  –  11 E2 proteins – 60-100 E3 proteins

See here:

The Ubiquitin–Proteasome System of Saccharomyces cerevisiae

Now, a very important point. Those 600+ E3 proteins that we find in humans are really different proteins. Of course, they have something in common: a specific domain.

From that point of view, they can be roughly classified in three groups according to the specific E3 domain:

  1. RING group: the RING finger domain ((Really Interesting New Gene) is a short domain of zinc finger type, usually 40 to 60 amino acids. This is the biggest group of E3s (about 600)
  2. HECT domain (homologous to the E6AP carboxyl terminus): this is a bigger domain (about 350 AAs). Located at the C terminus of the protein. It has a specific ligase activity, different from the RING   In humans we have approximately 30 proteins of this type.
  3. RBR domain (ring between ring fingers): this is a common domain (about 150 AAs) where two RING fingers are separated by a region called IBR, a cysteine-rich zinc finger. Only a subset of these proteins are E3 ligases, in humans we have about 12 of them.

See also here.

OK, so these proteins have one of these three domains in common, usually the RING domain. The function of the domain is specifically to interact with the E2-ubiquitin complex to implement the ligase activity. But the domain is only a part of the molecule, indeed a small part of it. E3 ligases are usually big proteins (hundreds, and up to thousands of AAs). Each of these proteins has a very specific non domain sequence, which is probably responsible for the most important part of the function: the recognition of the specific proteins that each E3 ligase processes.

This is a huge complexity, in terms of functional information at sequence level.

Our map of the ubiquinating system in humans could now be summarized as follows:

2 E1 proteins  –  40 E2 proteins – 600+ E3 proteins + thousands of specific substrates

IOWs, each of hundreds of different complex proteins recognizes its specific substrates, and marks them with a shared symbolic code based on uniquitin and its many possible chains. And the result of that process is that proteins are destined to degradation by the proteasome or other mechanisms, and that protein interactions and protein signaling are regulated and made possible, and that practically all cellular functions are allowed to flow correctly and smoothly.

Finally, here are two further compoments of the ubuquitination system, which I will barely mention, to avoid making this OP too long.

Ubiquitin like proteins (Ubl):

A number of ubiquitin like proteins add to the complexity of the system. Here is the abstract from a review:

The eukaryotic ubiquitin family encompasses nearly 20 proteins that are involved in the posttranslational modification of various macromolecules. The ubiquitin-like proteins (UBLs) that are part of this family adopt the β-grasp fold that is characteristic of its founding member ubiquitin (Ub). Although structurally related, UBLs regulate a strikingly diverse set of cellular processes, including nuclear transport, proteolysis, translation, autophagy, and antiviral pathways. New UBL substrates continue to be identified and further expand the functional diversity of UBL pathways in cellular homeostasis and physiology. Here, we review recent findings on such novel substrates, mechanisms, and functions of UBLs.

These proteins include SUMO, Nedd8, ISB15, and many others.

Deubiquitinating enzymes (DUBs):

The process of ubiquitination, complex as it already is, is additionally regulated by these enzymes which can cleave ubiquitin from proteins and other molecules. Doing so, they can reverse the effects of ubiquitination, creating a delicately balanced regulatory network. In humans there are nearly 100 DUB genes, which can be classified into two main classes: cysteine proteases and metalloproteases.

 

By the way, here is a beautiful animation of the basic working of the ubiquitin-proteasome system in degrading damaged proteins:

 

 

A summary:

So, let’s try a final graphic summary of the whole ubiquitin system in humans:

Fig 3 A graphic summary of the Ubiquitin System

 

Evolution of the Ubiquitin system?

The Ubiqutin system is essentially an eukaryotic tool. Of course, distant precursors for some of the main components have been “found” in prokaryotes. Here is the abstract from a paper that sums up what is known about the prokaryotic “origins” of the system:

Structure and evolution of ubiquitin and ubiquitin-related domains.

(Paywall)

Abstract:

Since its discovery over three decades ago, it has become abundantly clear that the ubiquitin (Ub) system is a quintessential feature of all aspects of eukaryotic biology. At the heart of the system lies the conjugation and deconjugation of Ub and Ub-like (Ubls) proteins to proteins or lipids drastically altering the biochemistry of the targeted molecules. In particular, it represents the primary mechanism by which protein stability is regulated in eukaryotes. Ub/Ubls are typified by the β-grasp fold (β-GF) that has additionally been recruited for a strikingly diverse range of biochemical functions. These include catalytic roles (e.g., NUDIX phosphohydrolases), scaffolding of iron-sulfur clusters, binding of RNA and other biomolecules such as co-factors, sulfur transfer in biosynthesis of diverse metabolites, and as mediators of key protein-protein interactions in practically every conceivable cellular context. In this chapter, we present a synthetic overview of the structure, evolution, and natural classification of Ub, Ubls, and other members of the β-GF. The β-GF appears to have differentiated into at least seven clades by the time of the last universal common ancestor of all extant organisms, encompassing much of the structural diversity observed in extant versions. The β-GF appears to have first emerged in the context of translation-related RNA-interactions and subsequently exploded to occupy various functional niches. Most biochemical diversification of the fold occurred in prokaryotes, with the eukaryotic phase of its evolution mainly marked by the expansion of the Ubl clade of the β-GF. Consequently, at least 70 distinct Ubl families are distributed across eukaryotes, of which nearly 20 families were already present in the eukaryotic common ancestor. These included multiple protein and one lipid conjugated forms and versions that functions as adapter domains in multimodule polypeptides. The early diversification of the Ubl families in eukaryotes played a major role in the emergence of characteristic eukaryotic cellular substructures and systems pertaining to nucleo-cytoplasmic compartmentalization, vesicular trafficking, lysosomal targeting, protein processing in the endoplasmic reticulum, and chromatin dynamics. Recent results from comparative genomics indicate that precursors of the eukaryotic Ub-system were already present in prokaryotes. The most basic versions are those combining an Ubl and an E1-like enzyme involved in metabolic pathways related to metallopterin, thiamine, cysteine, siderophore and perhaps modified base biosynthesis. Some of these versions also appear to have given rise to simple protein-tagging systems such as Sampylation in archaea and Urmylation in eukaryotes. However, other prokaryotic systems with Ubls of the YukD and other families, including one very close to Ub itself, developed additional elements that more closely resemble the eukaryotic state in possessing an E2, a RING-type E3, or both of these components. Additionally, prokaryotes have evolved conjugation systems that are independent of Ub ligases, such as the Pup system.

 

As usual, we are dealing here with distant similarities, but there is no doubt that the ubiquitin system as we know it appears in eukaryotes.

But what about its evolutionary history in eukaryotes?

We have already mentioned the extremely high conservation of ubiquitin itself.

UBA1, the main E1 enzyme, is rather well conserved from fungi to humans: 60% identity, 1282 bits, 1.21 bits per aminoacid (baa).

E2s are small enzymes, extremely conserved from fungi to humans: 86% identity, for example, for UB2D2, a 147 AAs molecule.

E3s, of course, are the most interesting issue. This big family of proteins behaves in different ways, consistently with its highly specific functions.

It is difficult to build a complete list of E3 proteins. I have downloaded from Uniprot a list of reviewed human proteins including “E3 ubiquitun ligase” in their name: a total of 223 proteins.

The mean evolutionary behavior of this group in metazoa is rather different from protein to protein. However, as a group these proteins exhibit an information jump in vertebrates which is significantly higher than the jump in all other proteins:

 

Fig. 4 Boxplots of the distribution of human conserved information jump from pre-vertebrates to vertebrates in 223 E3 ligase proteins and in all other human proteins. The difference is highly significant.

 

As we already know, this is evidence that this class of proteins is highly engineered in the transition to vertebrates. That is consistent with the need to finely regulate many cellular processes, most of which are certainly highly specific for different groups of organisms.

The highest vertebrate jump, in terms of bits per aminoacid, is shown in my group by the E3 ligase TRIM62. also known as DEAR1 (Q9BVG3), a 475 AAs long protein almost absent in pre-vertebrates (best hit 129 bits, 0.27 baa in Branchiostoma belcheri) and which flaunts an amazing jump of 1.433684 baa in cartilaginous fish (810 bits, 1.705263 baa).

But what is this protein? It is a master regulator tumor suppressor gene, implied in immunity, inflammation, tumor genesis.

See here:

TRIM Protein-Mediated Regulation of Inflammatory and Innate Immune Signaling and Its Association with Antiretroviral Activity

and here:

DEAR1 is a Chromosome 1p35 Tumor Suppressor and Master Regulator of TGFβ-Driven Epithelial-Mesenchymal Transition

This is just to show what a single E3 ligase can be involved in!

An opposite example, from the point of view of evolutionary history, is SIAH1, an E3 ligase implied in proteosomal degradation of proteins. It is a 282 AAs long protein, which already exhibits 1.787234 baa (504 bits) of homology in deuterostomes, indeed already 1.719858 baa in cnidaria. However, in fungi the best hit is only 50.8 bits (0.18 baa). So, this is a protein whose engineering takes place at the start of metazoa, and which exhibits only a minor further jump in vertebrates (0.29 baa), which brings the protein practically to its human form already in cartilaginous fish (280 identities out of 282, 99%). Practically a record.

So, we can see that E3 ligases are a good example of a class of proteins which perform different specific functions, and therefore exhibit different evolutionary histories: some, like TRIM62, are vertebrate quasi-novelties, others, like SIAH1, are metazoan quasi-novelties. And, of course, there are other behaviours, like for example BRCA1, Breast cancer type 1 susceptibility protein, a protein 1863 AAs long which only in mammals acquires part of its final sequence configuration in humans.

The following figure shows the evolutionary history of the three proteins mentioned above.

 

Fig. 5 Evolutionary history in metazoa of three E3 ligases (human conserved functional information)

 

An interesting example: NF-kB signaling

I will discuss briefly an example of how the Ubiquitin system interacts with some specific and complex final effector system. One of the best models for that is the NF-kB signaling.

NK-kB is a transcription factor family that is the final effector of a complex signaling pathway. I will rely mainly on the following recent free paper:

The Ubiquitination of NF-κB Subunits in the Control of Transcription

Here is the abstract:

Nuclear factor (NF)-κB has evolved as a latent, inducible family of transcription factors fundamental in the control of the inflammatory response. The transcription of hundreds of genes involved in inflammation and immune homeostasis require NF-κB, necessitating the need for its strict control. The inducible ubiquitination and proteasomal degradation of the cytoplasmic inhibitor of κB (IκB) proteins promotes the nuclear translocation and transcriptional activity of NF-κB. More recently, an additional role for ubiquitination in the regulation of NF-κB activity has been identified. In this case, the ubiquitination and degradation of the NF-κB subunits themselves plays a critical role in the termination of NF-κB activity and the associated transcriptional response. While there is still much to discover, a number of NF-κB ubiquitin ligases and deubiquitinases have now been identified which coordinate to regulate the NF-κB transcriptional response. This review will focus the regulation of NF-κB subunits by ubiquitination, the key regulatory components and their impact on NF-κB directed transcription.

 

The following figure sums up the main features of the canonical activation pathway:

 

Fig. 6 A simple summary of the main steps in the canonical activayion pathway of NF-kB

 

Here the NF-κB TF is essentially the heterodimer RelA – p50. Before activation, the NF-κB (RelA – p50) dimer is kept in an inactive state and remains in the cytoplasm because it is linked to the IkB alpha protein, an inhibitor of its function.

Activation is mediated by a signal-receptor interaction, which starts the whole pathway. A lot of different signals can do that, adding to the complexity, but we will not discuss this part here.

As a consequence of receptor activation, another protein complex, IκB kinase (IKK), accomplishes the Phosphorylation of IκBα at serines 32 and 36. This is the signal for the ubiquitination of the IkB alpha inhibitor.

This ubiqutination targets IkB alpha for proteosomal degradation. But how is it achieved?

Well, things are not so simple. A whole protein complex is necessary, a complex which implements many different ubiquitinations in different contexts, including this one.

The complex is made by 3 basic proteins:

  • Cul1 (a scaffold protein, 776 AAs)
  • SKP1 (an adaptor protein, 163 AAs)
  • Rbx1 (a RING finger protein with E3 ligase activity, 108 AAs)

Plus:

  • An F-box protein (FBP) which changes in the different context, and confers specificity.

In our context, the F box protein is called beta TRC (605 AAs).

 

Fig. 7 A simple diagram of the SKP1 – beta TRC complex

 

Once the IkB alpha inhibitor is ubiquinated and degraded in the proteasome, the NF-κB dimer is free to translocate to the nucleus, and implement its function as a transcription factor (which is another complex issue, that we will not discuss).

OK, this is only the canonical activation of the pathway.

In the non canonical pathway (not shown in the figure) a different set of signals, receptors and activators acts on a different NF-κB dimer (RelB – p100). This dimer is not linked to any inhibitor, but is itself inactive in the cytoplasm. As a result of the signal, p100 is phosphorylated at serines 866 and 870. Again, this is the signal for ubiquitination.

This ubiquitination is performed by the same complex described above, but the result is different. P100 is only partially degraded in the proteasome, and is transformed into a smaller protein, p52, which remains linked to RelB. The RelB – p52 dimer is now an active NF-κB Transcription Factor, and it can relocate to the nucleus and act there.

But that’s not all.

  • You may remember that RelA (also called p 65) is one of the two components of NF-kB TF in the canonical pathway (the other being p 50). Well, RelA is heavily controlled by ubiquitination after it binds DNA in the nucleus to implement its TF activity. Ubiquitination (a very complex form of it) helps detachment of the TF from DNA, and its controlled degradation, avoiding sustained expression of NF-κB-dependent genes. For more details, see section 4 in the above quoted paper: “Ubiquitination of NF-κB”.
  • The activation of IKK in both the canonical and non canonical pathway after signal – receptor interaction is not so simple as depicted in Fig. 6. For more details, look at Fig. 1 in this paper: Ubiquitin Signaling in the NF-κB Pathway. You can see that, in the canonical pathway, the activation of IKK is mediated by many proteins, including TRAF2, TRAF6, TAK1, NEMO.
  • TRAF2 is a key regulator on many signaling pathways, including NF-kB. It is an E3 ubiquitin ligase. From Uniprot:  “Has E3 ubiquitin-protein ligase activity and promotes ‘Lys-63’-linked ubiquitination of target proteins, such as BIRC3, RIPK1 and TICAM1. Is an essential constituent of several E3 ubiquitin-protein ligase complexes, where it promotes the ubiquitination of target proteins by bringing them into contact with other E3 ubiquitin ligases.”
  • The same is true of TRAF6.
  • NEMO (NF-kappa-B essential modulator ) is also a key regulator. It is not an ubiquinating enzyme, but it is rather heavily regulated by ubiquitination. From Uniprot: “Regulatory subunit of the IKK core complex which phosphorylates inhibitors of NF-kappa-B thus leading to the dissociation of the inhibitor/NF-kappa-B complex and ultimately the degradation of the inhibitor. Its binding to scaffolding polyubiquitin seems to play a role in IKK activation by multiple signaling receptor pathways. However, the specific type of polyubiquitin recognized upon cell stimulation (either ‘Lys-63’-linked or linear polyubiquitin) and its functional importance is reported conflictingly.”
  • In the non canonical pathway, the activation of IKK alpha after signal – receptor interaction is mediated by other proteins, in particular one protein called NIK (see again Fig. 1 quoted above). Well, NIK is regulated by two different types of E3 ligases, with two different types of polyubiquitination:
    • cIAP E3 ligase inactivates it by constant degradation using a K48 chain
    • ZFP91 E3 ligase stabilizes it using a K63 chain

See here:

Non-canonical NF-κB signaling pathway.

In particular, Fig. 3

These are only some of the ways the ubiquitin system interacts with the very complex NF-kB signaling system. I hope that’s enough to show how two completely different and complex biological systems manage to cooperate by intricate multiple connections, and how the ubiquitin system can intervene at all levels of another process. What is true for the NF-kB signaling pathway is equally true for a lot of other biological systems, indeed for almost all basic cellular processes.

But this OP is already too long, and I have to stop here.

As usual, I want to close with a brief summary of the main points:

  1. The Ubiquitin system is a very important regulation network that shows two different signatures of design: amazing complexity and an articulated semiotic structure.
  2. The complexity is obvious at all levels of the network, but is especially amazing at the level of the hundreds of E3 ligases, that can recognize thousands of different substrates in different contexts.
  3. The semiosis is obvious in the Ubiquitin Code, a symbolic code of different ubiquitin configurations which serve as specific “tags” that point to different outcomes.
  4. The code is universally implemented and shared in eukaryotes, and allows control on almost all most important cellular processes.
  5. The code is written by the hundreds of E3 ligases. It is read by the many interactors with ubiquitin-binding domains (UBDs).
  6. The final outcome is of different types, including degradation, endocytosis, protein signaling, and so on.
  7. The interaction of the Ubiquitin System with other complex cellular pathways, like signaling pathways, is extremely complex and various, and happens at many different levels and by many different interacting proteins for each single pathway.

PS:

Thanks to DATCG for pointing to this video in three parts by Dr. Raymond Deshaies, was Professor of Biology at the California Institute of Technology and an Investigator of the Howard Hughes Medical Institute. On iBiology Youtube page:

A primer on the ubiquitin-proteasome system

 

Cullin-RING ubiquitin ligases: structure, structure, mechanism, and regulation

 

Targeting the ubiquitin-proteasome system in cancer

Comments
To all: New complexity at the endpoint of the ubiquitin system: April 13, 2018 Structure and Function of the 26S Proteasome. https://www.ncbi.nlm.nih.gov/pubmed/29652515
Abstract As the endpoint for the ubiquitin-proteasome system, the 26S proteasome is the principal proteolytic machine responsible for regulated protein degradation in eukaryotic cells. The proteasome's cellular functions range from general protein homeostasis and stress response to the control of vital processes such as cell division and signal transduction. To reliably process all the proteins presented to it in the complex cellular environment, the proteasome must combine high promiscuity with exceptional substrate selectivity. Recent structural and biochemical studies have shed new light on the many steps involved in proteasomal substrate processing, including recognition, deubiquitination, and ATP-driven translocation and unfolding. In addition, these studies revealed a complex conformational landscape that ensures proper substrate selection before the proteasome commits to processive degradation. These advances in our understanding of the proteasome's intricate machinery set the stage for future studies on how the proteasome functions as a major regulator of the eukaryotic proteome.
Emphasis mine. Those who have followed the discussion will understand. :)gpuccio
April 14, 2018
April
04
Apr
14
14
2018
09:45 AM
9
09
45
AM
PDT
gpuccio Yes. It is the first post there at this moment.bill cole
April 14, 2018
April
04
Apr
14
14
2018
09:36 AM
9
09
36
AM
PDT
bill cole: OK, I will do it. Is it posted at TSZ?gpuccio
April 14, 2018
April
04
Apr
14
14
2018
09:26 AM
9
09
26
AM
PDT
gpuccio Here is my post at TSZ to Joe. We may not get an answer for a few days. April 14, 2018 at 4:56 pm
Joe Felsenstein, May I assume that gpuccio has not redefined “functional information” from Hazen and Szostak? Is there some reason to discard their definition? Bill Cole I don’t think he is hung up on the definition. He is trying to establish a way to measure it using living organisms. From listening to your lecture it appears that you have been aware of this issue for 40 years. You and gpuccio have been talking over each other at this point and maybe thats the best we can do for the time being. He is frustrated because you appear to be evasive. Tom posting your lecture is a clue why you are walking so carefully through this discussion. It appears that although you don’t completely agree with what Demski has concluded about functional information you do see value. Do you see value in the work gpuccio is doing? Where you the first to attempt a model of energy/information flow through living organisms?
If you have time it would be valuable for you to scan through his lecture.bill cole
April 14, 2018
April
04
Apr
14
14
2018
08:48 AM
8
08
48
AM
PDT
Joe Felsestein at TSZ:
I will be away from the keyboard, mostly, over the weekend — hope that by Monday gpuccio has cleared that up.
It's not difficult to clear it up. If you had just read my specific answers to you quoted at #885: my comments #828, #831, #847 and #882 instead of just reading and answering my #885, which was just a brief comment to ET, you would probably understand what I mean. My definition of functional information is not essentially different from the others, including Orgel, Abel, Durston, Szostak and, of course, Dembski: the concept is always to measure the complexity in bits that is necessary to implement some funtion. However, I have tried to make my definition empirically explicit. As mentioned many times, you can find my definition, and related clarifications, here: Functional information defined https://uncommondescent.com/intelligent-design/functional-information-defined/ The essential definition, quoted from that OP, is the following:
e) The ratio Target space/Search space expresses the probability of getting an object from the search space by one random search attempt, in a system where each object has the same probability of being found by a random search (that is, a system with an uniform probability of finding those objects). f) The Functionally Specified Information (FSI) in bits is simply –log2 of that number. Please, note that I imply no specific meaning of the word “information” here. We could call it any other way. What I mean is exactly what I have defined, and nothing more. One last step. FSI is a continuous numerical value, different for each function and system. But it is possible to categorize the concept in order to have a binary variable (yes/no) for each function in a system.
Of course, there are more details in the OP. My problem with you is about your statements quote in my comment #828: "That counts up changes anywhere in the genome, as long as they contribute to the fitness, and it counts up whatever successive changes occur." My comment (always at #828): "Again, are you kidding? So, if you have 500 different mutations of 1 AA in different proteins, each of them contributing in completely different and independent ways to fitness, you believe that you have 500 bits of complex functional information?" The question is rather simple, and I would appreciate to recieve an answer from you. To make it even more clear, I have given the example of the thief, to which, if I have not missed something, you have not answered (if you have, please give me a reference, because it's becoming very diffcult to check everything in the many different pages at TSZ). The thief mental experiment can be found as a first draft at my comment #823, quoted again at #831, and then repeated at #847 (to Allan Keith) in a more articulated form. In essence, we compare two systems. One is made of one single object (a big safe). the other of 150 smaller safes. The sum in the big safe is the same as the sums in the 150 smaller safes put togethjer. that ensures that both systems, if solved, increase the fitness of the thief in the same measure. Let's say that our functional objects, in each system, are: a) a single piece of card with the 150 figures of the key to the big safe b) 150 pieces of card, each containing the one figure key to one of the small safes (correctly labeled, so that the thief can use them directly). Now, if the thief owns the functional objects, he can easily get the sum, both in the big safe and in the small safes. But our model is that the keys are not known to the thief, so we want to compute the probability of getting to them in the two different scenarios by a random search. So, in the first scenario, the thief tries the 10^150 possible solutions, until he finds the right one. In the second scenario, he tries the ten possible solutions for the first safe, opens it, then passes to the second, and so on. A more detailed analysis of the time needed in each scenario can be found in my comment #847. So, I would really appreciate if you could answer this simple question: Do you think that the two scenarios are equivalent? What should the thief do, according to your views? This is meant as an explicit answer to your statement mentioned before:
"That counts up changes anywhere in the genome, as long as they contribute to the fitness, and it counts up whatever successive changes occur."
The system with the 150 safes corresponds to the idea of a function that include changes "anywhere in the genome, as long as they contribute to the fitness". The system with one big safe corresponds to my idea of one single object (or IC system of objects) where the function (opening the safe) is not present unless 500 specific bits are present. Please, answer at your ease. But answer.gpuccio
April 14, 2018
April
04
Apr
14
14
2018
04:07 AM
4
04
07
AM
PDT
Corneel at TSZ (about my new OP): "Will that be reposted here at TSZ?" I have posted it here. Anyone can post it, or parts of it, at TSZ. There is no copyright, it is public domain.gpuccio
April 14, 2018
April
04
Apr
14
14
2018
01:05 AM
1
01
05
AM
PDT
Entropy at TSZ: April 12, 2018 at 11:31 pm
So, he hadn’t examined “a few organisms,” he had examined “a few groups of organisms.” All against just humans. Hum.
Yes, I have tested the human proteome against specific groups of organisms (all known protein sequences in each group, IOWs the non redundant database of NCBI). Human proteins here are used as a "probe" to measure human conserved information against the times of divergence from the human line. I think I have explained that in detail many times. What's your problem?
Oh, and he has concluded that information has increased.
No. Of course not. I observe and describe the variations and the increase in human conserved information (the y axix in my plots), the only quantity that my procedure can measure. And I detail the conservation times (the x axis in my plots). I never say anything about a generic "increase" in some generic "information". My variables are very clearly defined. It's not my fault if you don't understand them.
Hum. So, he didn’t check a few organisms because he has a special definition for few, and he didn’t conclude that there was increases in information, but he has concluded that there’s “jumps” in information.
As I have explained, it was not a "few": it was all the known protein sequences for each explicitly defined group of organisms. I never concluded that there were "increses in information", a phrase that deos not mean anything. I have measured, in all cases, human conserved information in each explicitly defined group for each explicitly defined human protein. And I have observed, of course, big jumps in human conserved information. That's what I have done.gpuccio
April 14, 2018
April
04
Apr
14
14
2018
01:03 AM
1
01
03
AM
PDT
OMagain at TSZ: April 12, 2018 at 8:56 pm Oh, so someone is giving a look at my OPs about NS and RV! I commend you for doing that. You ask:
So my question is, can you give me an example of a specific biological sequence with 160 bits of functional information and explain how you know that sequence is functional? I assume that you know what it does, otherwise how do you know it’s functional information.
The alpha and beta chains of ATP synthase are an example O have often quoted. The beta chain has 663 bits of conservation between e. coli and humans, for example. See also my comment #713 for more details. And yes, I know what it does. Together with the alpha chain, it makes the main functional part of the F1 subunit of ATP synthase. Which builds ATP moòlecules from the energy deriving from a proton gradient.
And can you then give me another sequence with around half, or 80, bits of functional information and explain how you know it is functional information?
I can give you whatever you like, but I see no rreasons to search my database to find proteins with exactly 160 or 80 bits of functional information, unless you explain the reason for that. For the moment, the beta chain of ATP synthase will do.
I’ll then present to you a similar sequence. You can then presumably tell me if it’s actually functional information and if so how many bits it contains?
Not at all. This is a silly misunderstanding of ID theory. If you just give me a sequence, of AAs, bits, or whatever, in most cases I cannot know if it has any interesting or complex functions just form the sequence itself. For example, a sequence of AAs is well beyond my personal capacity of understanding how it will fold and what it will do (and, I would say, beyond the understanding of almost everyone). So I could never understand if any sequence of bits in machine language is functional or not. You see, the function is observed in the real world, not necessarily derived from the sequence itself. For proteins, scientists observe what the protein can do. Uniprot has a "function" section at the start of each protein page. I can look at it, ans so can you. For many proteins, function is not well known. In my reasonings here, I have often used conservation thorugh lonf evolutionary windows as a mark of function, even if the function itself is not known in detail. So, if you give me a siftwrae, I can easily test if it works and what it does, even if I don't know the source code. Of course, I must know the sequence and other indirect informations if I want to measure the specific functional information of a functional sequence for an observed, and explicitly defined, function.
If you are not able to do that not, why not? It seems to follow logically from your claims.
No, it doesn't, as I have shown.
Those are my questions.
Well, thank you for asking them.gpuccio
April 14, 2018
April
04
Apr
14
14
2018
12:49 AM
12
12
49
AM
PDT
Origenes: Thank you again for all your work, putting togetehre the most relevant moments of this long discussion. It is very appreciated! :) At least, it shows that I have really tried to answer many of their "arguments"!gpuccio
April 14, 2018
April
04
Apr
14
14
2018
12:24 AM
12
12
24
AM
PDT
DATCG: "I have catching up to do!" Please, take your time! :)gpuccio
April 14, 2018
April
04
Apr
14
14
2018
12:22 AM
12
12
22
AM
PDT
Gpuccio @911, Good find on specificity :) I have catching up to do! Might not participate much. For now, will continue to read and when time allows, add comments.DATCG
April 13, 2018
April
04
Apr
13
13
2018
06:20 PM
6
06
20
PM
PDT
The Skeptical Zone’s winning arguments — part IV:
Corneel: Alas, not true. Neo-darwinism is the theory of population change through natural selection put on more secure genetic footing than Darwin did. That doesn’t rely on common descent, I fear.
This sounds really strange. I have always thought that the step by step darwinian process does require CD. Could you explain better how it could take place if CD were not true? I don’t understand.[GPuccio]
Corneel: …
Dazz: Just keep regurgitating the same crap and pretend you’ve made a positive case for anything. Unbelievable.
GlenDavidson: The trouble is that the crucial premise [Natural systems where there is no obvious intervention of consciousness can generate complex functional information.] is not sound, it has not been shown to be true by the evidence. Indeed, the evidence is contrary to it ….
If “the evidence is contrary to it”, as you say, just provide a counter-example.[GPuccio]
GlenDavidson: …
GlenDavidson: Indeed, the evidence is contrary to it, since life is peculiarly lacking in aspects that one gets from observed designers.
What does “life is peculiarly lacking in aspects that one gets from observed designers” have to do with that?
GlenDavidson: … It would have to be legitimate first. You have to show that “No system of the a) type can generate complex functional information,” is actually true. If you’re using a false premise, there’s no falsification possible. And it’s at the least an unsound premise, as it has never had the evidence to demonstrate that it is so.
No, you are simply confused here. Falsifiability has nothing to do with the merits of a scientific theory. It just means that it is a scientific theory, because it is falsifiable. Please, check your philosophy of science.[GPuccio] - - Wrapping up this parade of nonsense … Two more “killer arguments” by Entropy:
Entropy: See that? He changed from asking about the function to asking about the protein. This way, instead of something as easy as getting new functions from already existing proteins, he’s asking for new proteins.
Entropy: The guy is a shameless ass-hole.
Origenes
April 13, 2018
April
04
Apr
13
13
2018
03:29 PM
3
03
29
PM
PDT
To all: While I have commented a little on E3 ligases in the new OP, I will continue to post interesting news about ubiquitin here. This is new, and brings us rather back in metazoa, to c. elegans: The UBR-1 ubiquitin ligase regulates glutamate metabolism to generate coordinated motor pattern in Caenorhabditis elegans. https://www.ncbi.nlm.nih.gov/pubmed/29649217
Abstract: UBR1 is an E3 ubiquitin ligase best known for its ability to target protein degradation by the N-end rule. The physiological functions of UBR family proteins, however, remain not fully understood. We found that the functional loss of C. elegans UBR-1 leads to a specific motor deficit: when adult animals generate reversal movements, A-class motor neurons exhibit synchronized activation, preventing body bending. This motor deficit is rescued by removing GOT-1, a transaminase that converts aspartate to glutamate. Both UBR-1 and GOT-1 are expressed and critically required in premotor interneurons of the reversal motor circuit to regulate the motor pattern. ubr-1 and got-1 mutants exhibit elevated and decreased glutamate level, respectively. These results raise an intriguing possibility that UBR proteins regulate glutamate metabolism, which is critical for neuronal development and signaling.
This is further evidence against the silly idea that E3 ligases are promiscuous and not specific: their defects are a cause of disease not only in humans, but even in nematodes!gpuccio
April 13, 2018
April
04
Apr
13
13
2018
01:57 PM
1
01
57
PM
PDT
Hi guys: I have been very busy writing the new OP, that has now been published. It is about a few important issues already patially discussed in this thread. I am rather tired, so I apologize if I will be rather slow in commenting (at least for a few hours!) :)gpuccio
April 13, 2018
April
04
Apr
13
13
2018
12:30 PM
12
12
30
PM
PDT
ET @908 At this point I would not trust any of these guys with understanding that 2 + 2 = 4. Felsenstein also 'understood' that 500-bits functional complexity arises "at once in one mutation" — yes really, see #882. Felsenstein seriously thought that this was GPuccio's argument. GPuccio had to tell Felsenstein that he never said that. You cannot make that stuff up. 500-bits by one mutation ... words fail me at this point.Origenes
April 13, 2018
April
04
Apr
13
13
2018
12:07 PM
12
12
07
PM
PDT
Joe Felsenstein:
I understand William Dembski’s “complex specified information” (in Dembski’s 2002-2007 arguments) as well as the altered version in his 2005-2006 paper.
Nonsense. Your natural selection paper on NCSE demonstrates that you do not understand Dembski at allET
April 13, 2018
April
04
Apr
13
13
2018
09:40 AM
9
09
40
AM
PDT
Don't forget that we don't know what information is even though we have gone through pain-staking detail explaining exactly what we mean.ET
April 13, 2018
April
04
Apr
13
13
2018
09:33 AM
9
09
33
AM
PDT
The Skeptical Zone’s winning arguments — part III:
Entropy: That is clearly pointing to a “gap.” Pointing to something you cannot understand how it can be done naturally. Sorry, but that’s not just god-of-the-gaps, but even classic god-of-the-gaps.
I would like to clarify a very important point: the “god-of-the-gaps” argument against ID and why it is completely false. … [GPuccio] — See #657
Entropy: Scientists have understood for quite a while that information arises from the dynamics between energy flows and the nature of physical/chemical “entities.”
Complex functional information? Really? Examples, please. If scientists “have understood” such a thing “for quite a while”, it will not be difficult for you to give examples. Do it. [GPuccio]
Entropy: For example, a substrate that it never encounters in its environment. However, once the correct substrate is found, it looks rather obvious in the efficiency / specificity of the enzyme towards it, compared to the “wrong” substrate. Where does all of this lead? To the realization that enzyme activities are not as perfect as presented in kinder-garden biochemistry, that they range in potential towards substrates other than their “normal” ones, and that, thus, there’s such a thing as “ladders” of specificity available for enzyme evolution. Not only that, after understanding this issue, it seems rather obvious.
There is nothing obvious in this confused fuss. You must explain how some new complex functional protein, for example a new protein superfamily, can arise by gradual steps, each of them giving an increase of function. Or at least why we should believe that it is possible. You only make generic and confused statements about enzymes. What is your point? [GPuccio]
Entropy: … physical interactions. They are also measured. Why would they if they’re so specific and perfect according to kinder-garden biochemistry? Shouldn’t we just see a complex and be done? Well, no, the formation of the complex depends on the relative concentrations of the proteins in question, which depend on their relative affinities towards each other. Wait! Relative affinities? Yes. They have pseudo-affinities towards other proteins. So, here, again, we see that there’s an obvious “ladder” for protein-protein interactions to evolve, and thus to the evolution of protein complexes.
Even more confusion. Is it possible? Affinities have nothing to do with that. We are speaking of naturally selectable functions. [GPuccio]
Entropy: I hope that gives you enough of a hint.
Not at all. Look, just an advice. Don’t give “hints”. Give answers. [GPuccio]
Entropy: Thus my emphasis. I see non-conscious systems doing that all the time. You seem to forget that this happens in life forms all the time with no consciousness involved. They put those amounts of information together with no conscious activity involved. Most life reproduces with no conscious activity involved. All life forms duplicate their DNA, transcribe it, translate the RNA into proteins, etc., thus putting together quite a bit of information, with no conscious activity involved.
Are you kidding? Do you even understand what you are saying? All life forms duplicate their DNA. Sure. They do that because: a) The information in their DNA is already there b) That information includes the information for DNA replication IOWs, they are only executing information that has been put together in their genomes. Not by them. Your statements are like saying that when I print a Shakespeare sonnet I am putting together the information in it. I, the great poet! Again, are you kidding? [GPuccio]
Entropy: I said that energy flow transforms into information. Complexity is what happens when systems out of equilibrium move towards equilibrium. For as long as equilibrium isn’t reached, we have information. Yes, that includes “functional” information.
No, it doesn’t. I mentioned writing because it is a clear and objective example of complex functional information beyond the 500 bits threshold. You do the same: give an example. But of course you can’t. [GPuccio]Origenes
April 12, 2018
April
04
Apr
12
12
2018
09:47 PM
9
09
47
PM
PDT
The Skeptical Zone’s winning arguments — part II:
Entropy: I did touch at least one, I explained that the semiosis you see is but an anthropomorphism.
It’s no anthropomorphism. It’s an objective property of the system.[GPuccio] See #590, #610
Entropy: Your claim is that the “complexity,” “functional information,” or whatever you want to call it, is beyond nature.
False. I never used that word [“beyond nature”]. [GPuccio]
Entropy: I haven’t seen a single life form that needs to consciously control its metabolism, or its ubiquitin-related processes.
This is really silly. Nobody, of course, is suggesting that animals, or humans, consciously control their metabolism, or similar things. [GPuccio]
CharlieM: How did the exact same 63 AA sequence come to appear in both species? Can the probability be estimated? I don’t know.
If you are saying that this is another empirical evidence for CD, I agree. But why say that while quoting me? I believe in common descent. How many times should I say that, to be believed? [GPuccio]
GlenDavidson: Basically, you assume that DNA is symbolic in God’s mind (yes, we know), and never imagine that a code might exist because, besides the ability of coded systems to store information compactly, sequential codes work very well for producing the sequences of proteins, among other things.
God’s mind has nothing to do with it. Or any mind, for that. Semiosis, as defined, is not a priori a mind thing. It is only empirically found in designed systems. [GPuccio] - - - - - GPuccio @903 I am not done yet. The best arguments are yet to come. :)Origenes
April 12, 2018
April
04
Apr
12
12
2018
03:06 PM
3
03
06
PM
PDT
keiths- if you think Wagner supports anything you say then it's up to you to show it. You can't even account for the proteins he usedET
April 12, 2018
April
04
Apr
12
12
2018
03:02 PM
3
03
02
PM
PDT
Origenes: Thank you for the nice summary! :)gpuccio
April 12, 2018
April
04
Apr
12
12
2018
01:31 PM
1
01
31
PM
PDT
The Skeptical Zone’s winning arguments — part I:
Entropy: Examining a few organisms, and comparing them to a few other, apparently less complex, ones, and concluding that information has increased, rather than reorganized, is quite a hasty conclusion.
A procedure and a conclusion that I have never done or stated.[GPuccio]
Entropy: You, however, think that just pointing to complexity will make your absurd imaginary friend into a reality.
In my view, instead, my argument is that there are three different markers that are linked to a design originn and therefore allow empirically a design inference (that is the basic concept in ID, and I have discussed it many times in all its aspects). Those three features are: a) Functional complexity (the one I usually discuss, and which I have quantitatively assessed many times in detail) b) Semiosis (which has been abundantly discussed by UB) c) Irreducible complexity In my OP I have discussed in detail a specific biological system where all those three aspects are present. Therefore, a system for which a design inference is by far the only reasonable explanation. [GPuccio]
Corneel: No, that is patently false. You are having your cake and eating it too. The “information jumps” that gpuccio introduces in his OP critically rely on the different genes he is comparing being homologs, i.e. on common descent being true. If he is unwilling to defend this, he must also drop that argument.
It is absolutely true that my argument here relies on common descent. I have clarified that I believe in common descent, and that I assume it for my biological reasonings. But there is more. I have defended Common Descent in detail and with the best arguments that I can think of. see my comments here, #525, 526, 529, 534, 538 and 546. What can I do more than that? [GPuccio]
OMagain: Please feel free to go into detail regarding these “severe limits” and how you have determined that they exist at all.
I have dedicated two whole OPs and long following discussions to the limits o NA and RV, with a lot of detail. Here they are: What are the limits of Natural Selection? An interesting open discussion with Gordon Davisson And: What are the limits of Random Variation? A simple evaluation of the probabilistic resources of our biological world Please, feel free to read them and to comment. I will answer. [GPuccio]
Entropy: If it was impossible for nature to put that amount of information together, then it would be impossible for designers to put that amount of information together. How so? Well, in order for designers to put that amount of information together, energy flow is necessary. Putting information together consists on “transforming” energy flow into patterns. We don’t produce available energy. We’re completely dependent on nature for that. So, claiming that a designer is necessary to produce “information,” seems a lot like putting the cart before the horse.
I can’t follow your reasoning. Yes, designers use energy to create patterns. And so? [GPuccio]
Entropy: So 500 bits? A joke for natural processes.
Then show one single example of that. [GPuccio]Origenes
April 12, 2018
April
04
Apr
12
12
2018
12:18 PM
12
12
18
PM
PDT
EugeneS: Me too! :)gpuccio
April 12, 2018
April
04
Apr
12
12
2018
11:10 AM
11
11
10
AM
PDT
GP We are on the same page here ;) I am glad.EugeneS
April 12, 2018
April
04
Apr
12
12
2018
10:42 AM
10
10
42
AM
PDT
bill cole: Yes, of course. I will include some more discussion about that in my next OP.gpuccio
April 12, 2018
April
04
Apr
12
12
2018
10:26 AM
10
10
26
AM
PDT
Bill- Dr Behe has dispensed with Thornton et al., alsoET
April 12, 2018
April
04
Apr
12
12
2018
09:51 AM
9
09
51
AM
PDT
Bill Cole- Structure, Function and Assembly of Flagellar Axial Proteins:
The bacterial flagellum is a biological macromolecular nanomachine for locomotion. A membrane embedded molecular motor rotates a long helical filament that works as a propeller driving the bacterium through the liquid environment. The flagellum is composed of about 30 different proteins with copy numbers ranging from a few to a few thousands and is made by self-assembly of those proteins.
Of course that "self-assembly" is unsupported...ET
April 12, 2018
April
04
Apr
12
12
2018
09:47 AM
9
09
47
AM
PDT
gpuccio Is this why you think the Hayashi paper supports your hypothesis?
The question remains regarding how large a population is required to reach the fitness of the wild-type phage. The relative fitness of the wild-type phage, or rather the native D2 domain, is almost equivalent to the global peak of the fitness landscape. By extrapolation, we estimated that adaptive walking requires a library size of 10^70 with 35 substitutions to reach comparable fitness. Such a huge search is impractical and implies that evolution of the wild-type phage must have involved not only random substitutions but also other mechanisms, such as homologous recombination. Recombination among neutral or surviving entities may suppress negative mutations and thus escape from mutation-selection-drift balance. Although the importance of recombination or DNA shuffling has been suggested [30], we did not include such mechanisms for the sake of simplicity. However, the obtained landscape structure is unaffected by the involvement of recombination mutation although it may affect the speed of search in the sequence space.
bill cole
April 12, 2018
April
04
Apr
12
12
2018
09:47 AM
9
09
47
AM
PDT
bill cole: I suppose Keiths is his greatest fan. :)gpuccio
April 12, 2018
April
04
Apr
12
12
2018
09:02 AM
9
09
02
AM
PDT
gpuccio
Wagner is beyond any sense.
Perfect for keiths :-)bill cole
April 12, 2018
April
04
Apr
12
12
2018
08:35 AM
8
08
35
AM
PDT
1 2 3 4 32

Leave a Reply