Mobile Startup Creates Censorship-Proof App to Help Activists And Crime Fighters
Update: Since the publication of this article, Facebook removed and blocked Cell 411 from allowing users to post live video to their own walls; the company is now trying to find a work around the Facebook block and still allow users to present live video streams to Facebook friends.

Startup Cell 411 Inc. (getcell411.com) has created a mobile app that makes it virtually impossible for governments, police and criminals to erase video which could serve as evidence of a crime or abuse. 

The app, called Cell 411, has been around for a number of months but the newly-released version has features that are unseen in any other mobile apps used by activists, aiming to fight censorship and also criminal activity.


*spoilers for ‘Bubbled’*

This weeks episodes are the best in the series in my opinion. I love getting background on Characters and the Gem War. And there was gorgeous background art in this episode.

What we learned:

  • Eyeball is a nugget of anger
  • Steven: “no signal? Come on, I’m right by a satellite!” even in cartoons the cell signal struggle is real
  • Eyeball (and possibly Jasper) came to Earth for closure.
  • Steven keeps a picture of Rose on his phone
  • It seems like Rose’s healing powers are rare. We don’t know if they’re rare for a Rose Quartz or rare for any Gems to have.
  • Eyeball: “they’re gonna give me my own Pearl!” I don’t think Pearls are exclusive to Diamonds, just upper class, important Gems.
  • Steven asked what would happen to him if something happened to his Gem. This is a good question, he’s half human, would he exist, just without Gem powers? Would Rose reform out of the Gem? 
  • It seems Rose did shattered Pink Diamond
  • When Steven was getting pulled into the Ruby Ship, the opening made a Pink Diamond symbol.

Garnet, talking about Rose shattering PD: “She didn’t always do what was best for her, but she always did what was best for Earth.”


A fortnight ago a biologist in Switzerland (Eduardo Moreno) tweeted he’d eat the first page of this paper if its hypothesis* was proved correct, and in reply one of the authors sent him a chocolate and marzipan replica :-)

Here’s a remedy in case you have to fulfil your pledge. Maybe it turns out that you are right, and you have to eat nothing (which I don’t hope). Maybe things are partly correct and partly not, and it would be a fraction of the page you have to consume. Then we could do it together, with a sip of Grappa.

* evidence for evolutionary adaptation of TRR-NFκB signaling modules in an organismal surveillance system that measures internal tissue fitness rather than external pathogenic stimuli.

Mending Myelin

The cable-like axons of nerve cells can carry electrical signals long distances. To speed up transmission in the central nervous system, support cells called oligodendrocytes wrap axons in an insulating material called myelin. But in diseases like multiple sclerosis, myelin is destroyed, making it difficult for nerve cells to carry messages.

In an effort to repair damaged myelin, scientists are searching for ways to nudge immature oligodendrocytes to become mature, myelin-producing cells. The image above shows a single oligodendrocyte (green) growing on a special plate with tiny, cone-shaped projections. Scientists use these plates — called micropillar arrays — because they can test whether various compounds will help oligodendrocytes grow and wrap around the cones in the same way they wrap around axons. This research may open the door for new therapies to regrow myelin in people with multiple sclerosis and other disorders where it is destroyed.  

Source: Brain Facts

Image Credit: Mei, et al. The Journal of Neuroscience, 2016.

Biochemistry with Olivia: G-Protein Coupled Receptors

G-protein coupled receptors (GPCRs) are a very diverse type of cell signalling receptor found only in eukaryotes, although the genes coding for GPCRs can be found in the genomes of prokaryotes as well. They sense ligand molecules in the extracellular environment and then activate signal transduction pathways inside the cell, leading to changes within the cell. GPCRs are the largest family of membrane proteins in the human genome, which contains over 800 different GPCRs (of which 460 are olfactory receptors). GPCRs can be divided into five main families: the rhodopsin family, the adhesion family, the frizzled/taste family, the secretin family and the glutamate family. The significant majority of GPCRs are in the rhodopsin family (701 of them, to be exact) and a large number of these GPCRs still have unknown physiological roles. 

Structure of GPCRs

GPCRs are integral membrane proteins containing seven membrane spanning helices. The N-terminus of the receptor protein is extracellular and contains the ligand-binding domain. The C-terminus is intracellular and is bound to the G-protein in the inactive state (when a ligand is not bound to the binding site). When a ligand binds, the receptor undergoes a conformational change that leads to the activation of the bound G-protein, which then detaches from the receptor. All GPCRs are structurally very similar, particularly in the transmembrane segments, although the ligands that bind to them can vary widely, from photons to proteins. 

What are G proteins?

G proteins are proteins which hydrolyse guanosine triphosphate (GTP) to guanosine diphosphate (GDP). Their role is to act as ‘molecular switches’ in cell signalling, and they can either be active (binding to GTP) or inactive (binding to GDP). There are two types of G-proteins, monomeric 'small GTPases’ and heterotrimeric G-proteins, composed of three subunits, the Gα subunit which has enzymatic activity and the Gb and Gy subunits which are tightly associated to form the Gby dimer. Only heterotrimeric G proteins are associated with G-protein coupled receptors, which acts as a Guanine Nucleotide Exchange Factor (GEF) by exchanging GDP for GTP in the active site of the Ga subunit of the G protein, and so activating it. There are several different types of Ga subunit, all of which perform different functions.

  • Gas subunit- activates the cAMP (cyclic AMP) dependent pathway by binding to and activating a protein called adenylate cyclase, which then converts ATP to cAMP. A large number of hormones are stimulated by the cAMP-dependent pathway, e.g. glucagon, which is involved in breaking down glycogen to glucose.
  • Gai subunit- inhibits the cAMP-dependent pathway by inhibiting adenylate cyclase
  • Gaq/11- activates phospholipase C (PLC) which then results in the activation of the inositol phospholipid dependent pathway.
  • Ga12/13regulate changes in the cell cytoskeleton and so are responsible for cell movement. 

GPCR signalling. Here are three examples of transmembrane signalling through G-protein-coupled receptors, with a little bit on their specific modes of regulation.

Adrenergic receptors couple to heterotrimeric G-proteins, which can be either stimulatory or inhibitory on adenylate cyclase, depending on the exact receptor: beta ARs stimulate AC; alpha ARs inhibit it. AC makes cAMP, the secondary messenger. cAMP binds protein kinase A, causing dissociation of its regulatory subunits and activation of the enzyme. cAMP is degraded to AMP by action of phosphodiesterase. Receptors are homologously desensitised by action of beta AR kinase, which phosphorylates cryptic phosphorylation sites on beta AR when adrenaline is bound. They are heterologously desensitised by downstream kinases which are only on when PKA is active.

The thrombin receptor is activated by N-terminal cleavage by thrombin. This reveals a TRAP sequence in the receptor, which acts as its own ligand. Phospholipase C beta is activated through a heterotrimeric G protein. (Note that phospholipate C gamma is activated through receptor tyrosine kinases.) PLC beta makes IP3, which releases calcium ions from the SR, ER, and/or cell exterior. Calcium ions activate protein kinase C. Calcium is removed from the cell by SERCA and PMCA ATPase calcium pumps.

The rhodopsin receptor in light-sensitive rod cells contains retinal, which undergoes a cis/trans isomerisation on incident light. Transducin carries the signal to phosphodiesterase, which extinguishes the second messenger cGMP. Loss of cGMP causes closure of cGMP-gated ion channels and stops calcium and sodium ion influx. This stops glutamate release, which allows the membrane to hyperpolarise, delivering the signal to the bipolar cell. Recoverin modulates the activity of guanylyl cyclase, which makes cGMP, by responding to the cellular calcium level. This allows the signal pathway to work in a range of ambient light conditions.

Abbreviations: Adr = adrenaline; GPCR = G-protein-coupled receptor; AR = adrenergic receptor; G = G-protein; AC = adenylate cyclase; ATP = adenosine triphosphate; cAMP = cyclic adenosine monophosphate; reg = regulatory subunit; cat = catalytic subunit; PKA = protein kinase A; beta ARK = beta adrenergic receptor kinase; Thr = thrombin; TRAP = thrombin receptor activatory peptide; PLC = phospholipase C; PIP2 = phosphatidylinositol 4,5-bisphosphate; IP3 = inositol 1,4,5-trisphosphate; DAG = diacylglycerol; SR = sarcoplasmic reticulum; ER = endoplasmic reticulum; PKC = protein kinase C; h = Planck’s constant; nu = frequency of light; PDE = phosphodiesterase; GC = guanylyl cyclase; GTP = guanosine triphosphate; cGMP = cyclic guanosine monophosphate; GMP = guanosine monophosphate; Glu = glutamate.

External image

Signalling through G-protein-coupled receptors von Ayraethazide ist lizenziert unter einer Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International Lizenz.

Mitochondrial disease, a bone of contention in the church apparently, is back in the news as UK legislation makes good progress towards the first treatment. The mitochondrial genome is a second outpost of the sum human genome, but isn’t unique as far as organelles are concerned. Some protists have a nucleomorph, while plants store genomic information in their plastids such as chloroplasts (though there are unexplained exceptions, described by Smith & Lee 2014).

Amazingly, the human mitochondrial genome is thought to be encoded in a single polycistronic sequence (something more heard of among viruses, bacteria and archaea, a nod to the endosymbiotic event that spawned the organelle) that is cleaved strategically in the liberation of 22 tRNAs (“tRNA punctuation“) to give the 13 mRNAs and 2 rRNAs (12S and 16S), a model first proposed by Ojala and Attardi in 1981:

Although all are transcribed on a single polycistronic RNA, the rRNAs end up far more abundant, suggesting an entirely unexplored mode of post-transcriptional regulation at work on the mitochondrial genome.

Michael Regnier wrote for the Wellcome Trust 3 years ago that “1 + 1 + 0.00001 ≠ 3”, likening the idea of mitochondrial replacement therapy meaning a child would have three parents to declaring heart transplant recipients have four. The distinction is real though (in my opinion), insofar as transplants aren’t at the embyronic level, so don’t diffuse into every cell of the adult… As Jeremy Farrar explained in a radio interview this week, the main reason we should be out in support of the technology is simply the extensive pre-clinical testing that’s gone into plans for mitochondrial medicine.

In oxidative phosphorylation (OXPHOS), ATP synthesis is coupled to electron flow through NADH or FADH2 cofactors en route to molecular oxygen. The electron carriers in the mitochondrial inner membrane respiratory assembly include quinones, flavins, FeS complexes, cytochrome protein haem groups, and copper ions. NADH’s electrons hop onto the flavin mononucleotide (FMN) prosthetic group of NADH-Q oxidorectase (“Complex I”), the first of a chain of four complexes that reduces ubiquinone (Q) as ubiquinol (QH2).

Similarly, complex II (succinate dehydrogenase, a citric acid cycle enzyme) pops electrons onto Q from FADH2 making yet more QH2, precipitating the hydrophobic electron carrier to spill them at Complex III, Q-cytochrome c oxidoreductase (which despite its name harbours cytochromes b and c1).

Cytochrome c is a more water-soluble mobile e carrier, and a peripheral membrane protein (meaning it adheres only temporarily at the mitochondrial inner membrane). Departing from the electron transport story, cytochrome c unlocks the cell’s death programme, apoptosis, in clustering with APAF1 and procaspase-9 to form an apoptosome.

The mitochondrial genome is frequently said to encode only parts of the mitochondrial bioenergetic machinery - components of the ETC. In humans, these genes reside on a single, circular, double stranded DNA molecule, but can also be linear, while cucumbers have 3 circular and individually autonomous mitochondrial chromosomes (below, an excerpt from Jennifer Mach’s 2011 editorial accompanying the finding).

They found that, unlike most plant mitochondrial genomes sequenced so far, which map to a single, circular chromosome, the cucumber mitochondrial genome contains three chromosomes (1556, 84, and 45 kb; see figure). These physically map as circles, although the exact structure may be more complex, as indicated by previous electron microscopy images of other plants showing branched, linear, and other structures. All of the intact, identifiable mitochondrial genes were found on the large chromosome, but all three appear to have transcribed sequences. The large chromosome is approximately twice as abundant as the smaller chromosomes (2.2 and 1.5 times as abundant), suggesting that they replicate independently. Sequence and DNA gel blot experiments showed that the two small chromosomes exist as both independent and cointegrated forms. Deep sequencing also allowed the authors to examine repeat-mediated recombination. The authors examined paired sequences from the ends of the same subclone, where the pairs do not map to the same location. Computational reconciliation of the ends showed many instances of recombination events meditated by many repeats, indicating highly active repeat-mediated recombination throughout the genome. Thus, this work explains how the cucumber mitochondrial genome got so large and provides new tools for examining the complex dynamics of these cool genomes.

mtDNA isn’t solely for ETC proteins though: the ~17,000 base pair sequence encodes 13 ‘canonical’ polypeptides involved in bioenergetics, but some smaller peptides lurk amongst them, and depart not only from electron transport but from the mitochondrion altogether.

Mitochondrial-derived peptides, or MDPs were discovered in 2001 - the first being humanin (rats have a homologue called rattin). Humanin takes on a host of antistress functions in humans, as brought together in a review from Lee et al. in 2013:

Humanin was first isolated from the surviving fraction of an Alzheimer’s disease patient’s brain (Hashimoto 2001, Guo 2003). It’s not yet clear whether mitochondrial or cytoplasmic translation is more often used, but since mitochondrial translation uses a slightly different genetic code, the mitochondrially translated 24 amino acid peptide produced is distinct from its cytoplasmic counterpart (it’s biologically effective in both forms, so the jury is out).

Mitochondrial signalling is far more elaborate than the common trope of ‘powerhouse’ or ‘battery’ gives credit. A host of proteins: Bak, Bcl-2, BH3-only (Puma, Nox, Bim, Bid) exert a layer of pro- and anti-apoptotic regulation integrated through a small protein Bax which if unsequestered pokes pores in the mitochondrial membrane, releasing the death-inducing cyt c into the cytoplasm.

Humanin is secreted in blood plasma and bound to cell membranes (where it fiddles around in receptor signalling), but inside tissues binds to Bax, preventing it causing harm to the host cell through bursting mitochondrial membranes.

Oddly, although this question came to me upon reading the news, a paper published only 2 days ago from a trio of French, Italian, and Canadian researchers covers the same topic, registering the potential for further unknown peptides to be lying in the mitochondrial genome. After reviewing the role of humanin, they describe the gau gene:

In addition to its ubiquitous presence, the reason why the gene has been named gau for Gene Antisense Ubiquitous, strong arguments indicate that the gau ORF indeed encodes a functional protein: (i) it is evolving under purifying selection, (ii) the deduced GAU proteins share some conserved amino acid signatures and structure among different taxa, suggesting a possible conserved function, (iii) gau has been identified in sense-oriented ESTs with poly(A) tails, (iv) immunohistochemical experiments using an anti-GAU monoclonal antibody showed a mitochondrial-specific signal in human cells, and (v) BLAST analyses suggested that no part of any known human proteins exhibits a high level of amino acid identity with the peptide antigen that was used for immunization and antibody production (Faure 2011). As for humanin, potentially functional and similar but not identical gau regions have been found in the nuclear genome. However, none of the deduced protein sequence possesses a mitochondrial signal peptide (Faure 2011), a result that is not in line with the intramitochondrial localization of GAU observed using the anti-GAU antibody. According to the authors, the most parsimonious hypothesis is that gau is a mtDNA-encoded protein gene, providing evidence for antisense overlapping functional open reading frames in mitochondrial genomes.

Other than this well-studied pair, bioinformatics has brought sequence-level suggestions of extra-canonical proteins, “small nuclear open reading frames (sORFs) encoding biologically active peptides of 11–32 amino acids in length” (Andrew & Rothnagel, 2014; Kondo et al., 2010).

Lastly, there’s suggestion that further mitochondrial proteins may be encoded through tetracodons (Seligman, 2012a,b), backed up by observation that this class of tRNA (which feature 4 interacting nucleosides at the anticodon loops rather than the regular 3 base code) is seen to evolve along with the number of sequences that would potentially form tetracoded mitochondrial open reading frames (ORFs).

(Image caption: An illustration of the dopamine transporter in its outward- (left) and inward-opening (right) state. Note that the inward opening has brought about an outward closing and change in the number of water molecules (blue and pink spheres) inside and outside the cell)

Opening Neurotransmission’s Gatekeepers

In an era of instant communication, perhaps no message-passing system is more underappreciated than the human body. Underlying each movement, each mood, each sight, sound, or smell, an army of specialized cells called neurons relays signals that register in the brain and connect us to our environment.

Neurons communicate by emitting signals composed of chemicals called neurotransmitters. To do their job, neurotransmitters must leave the cell, cross a gap known as a synapse, and bind to an adjacent neuron’s receptor. Like the long and short signals of Morse code, neurons modulate the bursts of neurotransmitters they send to convey more complex information. Separating one signal from another, however, requires each preceding signal to be sucked out of the synapse. To save energy, this is achieved by a process known as reuptake, in which the released neurotransmitter is transported back into the releasing neuron.

So what serves as the synapse’s vacuum cleaner? Membrane proteins called neurotransmitter transporters act as the gatekeepers of neurotransmission and contribute to the movement of chemicals into and out of the cell.

Understanding how these gatekeepers function—both as energy-driven systems and as key machinery in biological processes—is one of the primary aims of Harel Weinstein’s laboratory at the Weill Cornell Medical College of Cornell University. For nearly two decades, Weinstein’s lab has taken a physics-based approach to the problem, constructing 3-D models of a specific family of neurotransmitter transporters called neurotransmitter sodium symporters (NSS) and simulating their actions and interactions using supercomputers.

By observing these complex molecular machines in action using high-performance computing, Weinstein and his collaborators are not only learning how cells harness energy to move molecules against a concentration gradient (from low to high concentration) but also uncovering potential strategies for treating behaviors such as addiction, depression, and disease-related mutations associated with disorders of the NSS proteins.

In 2015, Weinstein’s team used the Titan supercomputer at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) to produce the first end-to-end simulation of a sodium ion, the fuel that powers NSS, moving from the synapse into the cell via the dopamine transporter (DAT), the gatekeeper for the neurotransmitter dopamine that is associated with reward-motivated behavior.

Titan is the flagship supercomputer of the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility located at ORNL. To study neurotransmitter reuptake in depth, Weinstein’s team needed access to Titan’s thousands of GPUs to calculate hundreds of atomistic models simultaneously.

“This was a major development because until now we could only look at the general properties of the transporter,” Weinstein said. “In our simulation on Titan, we were able to incorporate new experimental data into our DAT model and follow its movements over long periods of time, which made all the difference.”

Adding data to DAT

Embedded within the cell membrane, DAT straddles two environments with one notable difference between them: sodium concentration. Outside the cell, numerous sodium ions float freely. Inside the cell, sodium is comparatively scarce. During reuptake, the difference in concentration presents a source of potential energy for the transporter. By coupling the electrochemically favorable movement of sodium ions (high to low concentration) to the unfavorable movement of dopamine, the transporter gets the energy to carry out its task.

The general outline of this cotransport, known as symport, is well documented, but the underlying organization and coordination of the atoms that make this action possible are not. “The key question we are asking is, ‘What are the changes in the structure of the transporter molecule that allow this coupled transport to occur?’” Weinstein said.

By incorporating the most recent experimental data into a computational model, Weinstein’s team is finding answers. To construct its most recent model, the team worked with results from x-ray crystallography and from measurements of DAT function carried out by Vanderbilt University School of Medicine’s Aurelio Galli. The collaboration resulted in a detailed description of a previously unknown contribution of a DAT section known as the N-terminal loop interacting with negatively charged lipids in the cell membrane called phosphatidylinositol 4,5-bisphosphate, or PIP2.

Experimental research led by Galli demonstrated that the cross talk of these two components plays an important role in the ability of the DAT to release the sodium into the cell interior. The computational simulations showed what these roles are and how the mechanism works.

“The remarkable finding from the new data is that the N-terminal loop interaction with PIP2 occurs at one far end of the DAT molecule but affects sodium movement at a distance. Such an indirect mechanism that allows chemical action at a distance is called allostery, and while its existence was suspected, the detailed information gathered from the computational runs allowed us to understand it in atomic detail,” Weinstein said. “This allosteric effect is linked to the opening of the intracellular gate, and the mechanism we uncovered now suggests a completely new way of manipulating the transporter.”

Weinstein’s team examined and validated this new model under an allocation on Titan awarded through DOE’s Office of Advanced Scientific Computing Research Leadership Computing Challenge program.

To maximize the use of its time on the OLCF’s Titan, a Cray XK7 capable of 27 petaflops (or 27 quadrillion calculations per second), Weinstein’s team turned to the molecular dynamics application ACEMD, a GPU-friendly code created by the European software company Acellera. ACEMD relies almost exclusively on the ability of GPUs to quickly execute repetitive calculations to solve complex problems, allowing researchers to obtain solutions in much less time than would a conventional molecular dynamics code.

To reduce its time to solution even more, Weinstein’s team simulated 100 to 300 copies of its DAT system at a time, and under varying conditions, with each copy occupying a single Titan GPU. With these simulations, ACEMD tracked the movement of between 200,000 and 800,000 atoms over the course of more than 200 microseconds.

“Titan gave us a very large number of GPUs, which we could run for a very long time. Without this, our project just couldn’t work,” Weinstein said.

By analyzing DAT’s atomic interactions under varying temperatures and environments, Weinstein’s team covered a large swath of the molecule’s configurational space, or DAT’s possible range of motion. Using free-energy sampling methods (e.g., metadynamics and umbrella sampling) to identify the predominant movements, or modes, within the biomolecules’ complex configurations, the team created a complete microsecond-scale simulation of DAT function several orders of magnitude larger and longer than anything previously achieved.

Malfunctions and mutations

Putting the DAT puzzle together is the first step. Finding new connections between its component atoms that are responsible for the performance of the molecular machinery as a whole is the next one.

To conduct this kind of post-simulation analysis, Weinstein’s team uses its own analytics platform called N-body Information Theory. The software is configured to comb through DAT simulation data in real time and find correlations between movements of distant atoms. Identifying these patterns can provide insight into how one part of the DAT machine affects another and thereby assembles the allosteric mechanism. It can also prove useful when studying how mutation-related malfunctions manifest in the molecule.

Currently, Weinstein’s team is applying its updated model to DAT mutations related to diseases such as autism, Parkinson’s disease, and attention deficit hyperactivity disorder, which have been shown to be affected by malfunctions of the neurotransmission process.

“Because we are gaining a clearer picture of how the transporter works at the molecular level, we are able to understand exactly how these mutations found in patients do the damage that they do. At the same time, we learn how energy is gained, stored, and used in the molecular machines that perform the biological functions,” Weinstein said.

Related Publications:

LeVine, Michael V., et al., “Allosteric Mechanisms of Molecular Machines at the Membrane: Transport by Sodium-Coupled Symporters.” Chemical Reviews (2016).

Khelashvili, George, et al., “Spontaneous Inward Opening of the Dopamine Transporter is Triggered by PIP2-Regulated Dynamics of the N-terminus.” ACS Chemical Neuroscience 6, no. 11 (2015): 1825–1837.

Stolzenberg, Sebastian, et al., “Mechanism of the Association Between Na+ Binding and Conformations at the Intracellular Gate in Neurotransmitter: Sodium Symporters.” Journal of Biological Chemistry 290, no. 22 (2015): 13992–14003.

Cell Signalling

DEFINITION: Cells communicate with one another by sending and receiving signals

  • Cells must have modified protein molecules attached to their surface, in order to be able to receive signals known as RECEPTORS
  • Hormones areused in this communication process
  • Any cell with a receptor for these hormones is known as a target cell
  • If a hormone binds to a target cell, it’s because they have complementary shapes
  • ^ at the end it’s meant to say reduced blood sugar levels…
  • EXAM QUESTION: Nicotine has an effect on nerve cells, but not on other types of cell in the body, use your knowledge of cell membrane structure to explain why. (3 marks)
Nicotine only binds to receptors with a complementary shape. Since different cells have different membrane-bound receptors, nicotine will only affect nerve cells as only they have the correct receptor for it.

Q: Many proteins used in signal transduction are made up of domains that act as separate functional modules. Describe how two types of these modular domains work, including why their modularity is useful for their function.

Two prominent examples of autonomously folding units with conserved catalytic functions — the broad consensus definition of a ‘domain’ — found in signaling proteins are the SH2 (Src homology 2) and SH3 (Src homology 3).

Pawson (2001) described in detail how SH2 domains function as interaction modules.

Short linear motifs (SLiMs) are functional peptide modules some 3-10 amino acids long, that most commonly function through interaction with a specific globular protein domain. Two of the best known cases of such interactors are SH2 (src homology 2) and SH3 (src homology 3) domains. SLiMs may act as sites for post-translational modification or register binding events.

The binding function can be dissected into three types of signalling: targeting signals (such as the nuclear localisation signal), degron motifs (such as KEN-box degradation motifs) and ligand-binding sites.

SH2 and SH3 fall into the latter class (indeed ligand-binding is the most commonly seen function of the three).

Astonishingly, most of the binding specificity and affinity of a SLiM is found in a still smaller portion of the 3-10 residues, a core of more like 2-5 residues. Weatheritt, Babu, Gibson and Tompa have provided a broad description and several reviews of these modules in recent years, and have repeatedly drawn attention to the presence of intrinsic disorder at the peptide regions of these motifs. Late last year a census of peptide motifs from Babu and Tompa suggested these modules were present across the proteome on the order of over a million, making them a staggeringly understudied aspect of the cell.

Approximately 110 SH2, and 280 SH3 domains are found in the human genome. Each of these domains can bind peptide motifs in dozens of different partners (though not all motifs can be assumed functional).

SH2 mediate phosphorylation-dependent signaling, and were the first type of peptide-recognition domains discovered (Pawson 2002). Further to the recognition of phosphorylated tyrosine (pTyr), different SH2 instances exhibit preference on the basis of a handful of residues C-terminal to the pTyr (the ELM Eukaryotic Linear Motif database distinguishes 6 SH2 ligand classes). In this manner, sequence context of autophosphorylation sites of a receptor for example can also act to determine which SH2-containing targets it recruits, thus which biochemical pathways it activates.

This selection is only curtailed by the contribution of shape complementarity of ligands, placing some restriction on taking a purely primary sequence-based predictive view here.

Modularity is enhanced in this case by the phosphorylation PTM, since a major portion of binding energy is contributed by interaction with the modified residue such that the consensus motif can be shorter than would otherwise be permissible (SH2 domains can bind unmodified ligands, but with weaker affinity and requiring larger peptide-binding surfaces).

Unlike many protein-protein interactions, the hydrophobic effect does not drive protein-protein interaction in SH2 phosphorylation. Sitting in a deep pocket, the Tyr residue in question interacts through a charge-driven mechanism. Transient binding is often associated with a smaller hydrophobic residue content at interfaces.

SH3 binds proline-rich peptides that form a polyproline type II helix (the classic SH3 consensus motif is PxxP, x xbeing an arbitrary residue), though variants are observed which will bind the domain in opposite orientations.

SH3 binding can alter localisation, such as increasing residence time of some proteins at the cell surface for example in the case of a cancer-associated splice variant of adam-15 [a metalloproteinase].

It’s fitting to describe these two domains together in regard to modularity, as they are a fine example of the further degree of modularity obtained in combining peptide recognition domains.

In the Src tyrosine kinase which bestowed SH2 and SH3 their names, an SH2 domain binds the phosphorylated C-terminus and an N-terminal SH3 domain binds the linker between SH2 and the kinase domain.

Amusingly, the prototypical polypeptide for SH- domains does not hold true to the PxxP ‘consensus’ in this linker (the polyPro type II helix forms regardless, which interacts with the SH3 domain).

Neither of these intramolecular interactions are sufficient individually, but when combined they render the kinase domain inactive. The autoinhibitory interactions are distant from the kinase active site, but attenuate its activity, in part by clamping the kinase domain (SH1) in a rigid state precluding the dynamic motions crucial for substrate phosphorylation.

What’s more, these interactions suppress undesirable binding of the interaction domain.

Their repression is only alleviated upon dephosphorylation of the C-terminus [causing the SH2 to dissociate, leaving just the individually insufficient SH3], or coaxing of the SH2/SH3 domains away by extramolecular ligands (which break the inhibitory grip of the phosphorylated tail and linker region). The most potent such activators are multivalent peptides in which SH2 and SH3 binding sites [the extramolecular ligands] correctly spaced for both domains, which imparts high specificity and compounds the utility of modular domains as mediators transient, yet high specificity signalling (cooperativity in low affinity interactions produces high avidity).

Functionally, theoretical biologists have proposed that avidity-sensing switches such as this allows proteins to quantify stoichiometry (modulating signal transmission in line with ligand concentration).

Great read: Stein et al. (2009) Dynamic interactions of proteins in
complex networks: a more structured view
. FEBS, 276 5390-5405

More questions answered

It may be necessary not to think of the pathways as sharply and precisely delineated from the broader cellular network, but rather to keep in mind that a pathway representation may always be a “warm, fuzzy, cloud”: that is to say, warm since the answer is close but not necessarily exact; fuzzy, since the membership of components in a pathway is graded; and a cloud, since the boundaries are not sharply defined.

Kleensang et al (2013) Pathways of Toxicity, a report from t4 - The Transatlantic Think-Tank for Toxicology.

This quotation also referenced Kholodenko’s paper in Science SignallingComputational Approaches for Analyzing Information Flow in Biological Networks, in which the authors write that “the life of a biologist has changed”…

Just 20 years ago, the standard modus operandi was one research team working on one protein or gene for a lifetime of research.

… Mathematical and computational modeling methods are playing major roles in both tasks, especially as the ability to generate data has outpaced our ability to interpret them. The greatest strides have been made in the first task. The second is lagging behind but is moving into the limelight as new analysis methods are developed.

… Historically, traditional hypothesis-driven experiments formed the early basis of information about signal transduction pathways by piecing together components in tedious trial-and-error type experimental approaches that assume that signaling inputs are related to outputs by a linear path of signal transduction. Two experimental mainstays that have rapidly and dramatically enhanced the ability to map the components of interaction networks are the genetic yeast two-hybrid (Y2H) system and mass spectrometry (MS)–based proteomics.

False-positive results and the absence of known protein-protein interactions (PPIs) that depend on contextual information (such as stimulus-specific phosphorylation, which may or may not occur within budding yeast) remain limitations of the method. However, as the above example shows, these shortcomings are being addressed. The abundant data that have been and continue to be accumulated are of great utility, particularly when combined with other types of interaction data. An additional limitation of the Y2H approach is that it cannot reveal dynamic changes in PPIs; thus, the resulting graphs of binary interactions do not reveal how signaling information flows, which hinders the reconstruction of directed pathways.

MS analysis of cells fractionated into defined subcellular signaling structures—such as the centrosome, the mitotic spindle, and the kinetochore, for example—has identified many of the critical protein components of these subcellular complexes. Focused isolation approaches—particularly the use of tandem affinity purification (TAP)–tagged proteins, combined with stable isotope labeling by amino acids in cell culture (SILAC)—are sensitive, specific, and accurate methods for identifying proteins that interact with particular signaling molecules. A caveat of most MS-based methods is that the experiments generally do not reveal which interactions are direct and which are mediated through an additional component or components. 

Perhaps the best approach to defining network topology is the weighted collection of the available evidence for specific protein-protein interactions, including the results of both the low-throughput and high-throughput approaches. Fortunately, a number of online databases and Web-based tools that enable the construction and analysis of weighted collections exist, including BIND, BioGRID, MINT, and DIP. Some tools, such as STRING and iRefWeb, attempt to merge the information from different databases, as well as text-based searches from the literature into a single Web resource. Similarly, resources for pathways and pathway models are constantly being improved. Some examples are the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, Pathway Commons, the Science Signaling Database of Cell Signaling, the Pathway Interactome Database, and BioModels.

The scale of most of these data collections is enormous, and the amount of potentially valuable information in them is huge. However, they are complicated by the presence of misidentified peptides and proteins, incomplete representation of known network components, and an inherent bias against proteins that are present in low abundance… [None] of these approaches enables the reconstruction of network models that enable dynamic simulations.

High-throughput data usually suffer from low information content—that is, the observable results contain little information about the unknown parameters that caused them. A “local” perturbation that is initially confined to a particular network node can propagate and cause widespread “global” changes in the network and thereby mask immediate connections and routes. This issue is particularly pertinent to large omics data sets, because even in response to a single local perturbation, the omics snapshots of the cellular state arise from a plethora of interactions spreading through cell networks.

From thereon in gets quite dense but engrossing if you’re interested in understanding “systems biology”, and although encased in computational terminology many of the ideas are quite intuitive. As the paper points out - all of the life sciences are grappling with this issue of a sudden data deluge. Wonder is a matter of personal taste, but this is fascinating to me:

One of the challenges of mechanistic modeling is a combinatorial explosion in the number of emerging different species and distinct states of cellular networks that include scaffolds and proteins with several posttranslational modification sites. These multiple docking and modification sites generate a variety of heterogeneous protein complexes, and each of these complexes can be involved in many parallel reactions. Even initial steps in signal transduction that include receptors and adaptor proteins may generate hundreds of thousands and millions of distinct states, referred to as “micro-states” of a network.

… Many topological motifs enriched in transcriptional networks have also been found in RTK, mitogenic [Ras to ERK], and survival [PI3K] signaling networks. Although different underlying biochemistry results in distinct kinetic equations that describe signaling or gene networks, the control circuitry remains similar.

Having said this, some of the field’s problems are particularly gruesome:

It is difficult or impossible to compare networks composed of different types of signaling components. Protein networks are typically interaction networks or modification networks. Like transcriptional networks, they often have a time component or compare different conditions. However, it is difficult to compare transcriptional and protein networks directly, even when frequent parallel sampling is available.

The time delay between the production of mRNA and its encoded protein can vary between genes and, hence, tends to destroy protein and gene regulatory network comparisons based on temporal correlations. In addition, because high-throughput proteomics identifies proteins on the basis of isolated peptide fragments, rather than coverage of the complete protein sequence, matching proteins to splice variants easily seen in transcriptomics experiments becomes ambiguous. Metabolites are direct outputs of protein activities and, therefore, would be expected to map organically onto protein networks. However, metabolic networks model physical fluxes of metabolites, whereas protein networks model flow of information or abstract influences. These relations cannot be directly superimposed, and also, the mathematical methods for analyzing these networks are fundamentally different.

It seems that we are stranded with a treasure chest of information and many keys that do not fit.

The results from genome sequencing projects have not only destroyed the one gene–one protein–one function dogma, but also bluntly shown us that our mere 20,000 genes perform the huge diversity of biological tasks by combinatorial cooperation whose complexity simply stuns the human mind.

& lastly an interesting little fact…

Although several thousand proteins are estimated to be susceptible to a drug, currently used drugs only target ~270 human proteins, a number that has not changed in almost a decade.

GoTenna, the startup that lets you text without cell signal, raises $7.5M and launches with REI

GoTenna, the device for people who want to stay connected when they don’t have cell signal, has found a natural launch partner — outdoor equipment retailer REI. Daniela Perdomo, goTenna’s CEO and co-founder, told me that REI has signed on to be the startup’s exclusive retail launch partner. For the next three months, the only place you’ll be able to buy goTenna,… Read More



After seeing what she saw Amazon used the tech she hid in the forest to track this guy’s phone number from signals the cell phone was giving off. A parasite no doubt… and his name was Remix? Amazon pulled a cell phone out of her pocket and called the number.

🐆 Hello! Are you the neon clad Remix that just kick The Alpha’s ass?

Steven Universe: Bubbled

my precious steven

Angry Ruby™


He acts like there would be a cell signal in outer space

Tell me you dont see the resemblance


“Great! This is just perfect!” The ultimate Ruby quote

Nice dagger Rube

Steven is constantly under so much stress…my little baby ;-;


Ame and Pearl crying


Love Like You™

This show is so deep

this show makes me wanna cry but i love it

Akilina walked through the forest, finally getting away from her classmates. She was sick of everyone trying to get her out of the house and to parties; she just wanted to be alone for the moment. So she started slipping into the woods after school were the cell signal was weaker so she didn’t even get bombarded with texts or phone calls while she tried to unwind. It was easier to get lost in your thoughts when there were no interruptions.

So lost in her thoughts, she didn’t notice the person running towards her, causing her to fall to the ground and snap back to reality. “Hey!” She yells.