Nobel Price for molecular machines

On Wednesday this week, the Nobel Prize in Chemistry 2016 was awarded to Jean-Pierre Sauvage, Sir J. Fraser Stoddart, and Bernard L. Feringa for the design and production of molecular machines. What are molecular machines? These tiny machines are a thousand times thinner than a strand of hair and made of linked molecules with movable parts. 

Since the mid 20th century, chemists have been attempting to produce molecular chains in which ring-shaped molecules were linked together. Normally, covalent bonds hold the atoms in molecules together. In these chains, chemists wanted to create mechanical bonds, where molecules were interlocked without directly interacting with each other. In 1983, Jean-Pierre Sauvage used a copper ion to create molecular chains with a yield of 42%. These molecular chains, called catanenes, were early type of non-biological molecular machine. In 1994, Jean-Pierre Sauvage’s group succeeded in producing a catenane in which one ring rotated around the other ring when energy was added.

The second major step was completed by Fraser Stoddart in 1991, when he developed a rotaxane, in which a molecular ring was threaded onto a thin molecular axle. An electron-poor ring was threaded around an electron-rich axle. The addition of heat could be used to control the movement of the ring along the axle. Molecular lifts, artificial muscle and a molecule-based computer chip have since been created using rotaxanes.

Bernard Feringa was the fist person to develop a molecular motor in 1999. The motor consists of two flat chemical structures joined by a double bond between two carbon atoms. Methyl groups attached to each rotor blade function as ratchets that keep the molecule to keep rotating in the same direction. Exposure to UV light pulses cause the rotor blades to move 180 degrees around the central double bond. His group has optimized the motor so that it now spins at 12 million revolutions per second. Using molecular motors, he has rotated a glass cylinder that is 10,000 times bigger than the motor and also designed a nanocar.

The Laureates have started a toolbox of chemical structures that can be used to build increasingly advanced creations, such as a molecular robot that can grasp and connect amino acids and intricate webs of molecular motors that wind long polymers.

In the 1830s, when the electric motor was at the same stage, scientists could display various spinning cranks and wheels, but had no idea that the electric motor could like to instruments like washing machines, fans, and food processors. It’s interesting to imagine what the future could hold for molecular machines!



Is Left the Right Side?

Most people aren’t familiar with the problems left-handed individuals face: ink on your hands, uncomfortable scissors and the computer mouse that wasn’t made for you. Only about 10-15% of humans have a preference for the left hand and for centuries have been demonized, forced to learn using the “right hand” and suffer from higher accident rates when using equipment that was designed for right-handed people. Why? It’s still a mystery why there is such an imbalance favoring the right hand, but archeological evidence suggests that the phenomenon is not new. Neandertals seem to have had an abundance of tools designed to be used with the right hand.

Humans aren’t unique in their preference for one side over the other: Many animals use one side of their extremities more frequently, and like for humans, the asymmetry is evident in the brain, where the “opposite” side hosts the dominant motor control areas. One particularly curious example are elephants, which have their own interpretation of handedness: When they rip out greens from the ground, they use their trunk to wrap it around the grass and pull. They can now either wrap their trunk clockwise or counterclockwise around the food and surprisingly most individuals only ever go in one direction. It is pretty evenly distributed and there doesn’t seem to be such a strong imbalance as with human handedness. Oddly enough, it has been found that elephants without such a side preference (they do exist!) are at a disadvantage, because they feed significantly slower. A possible explanation is that through the lateralization of the behavior, the neuronal circuits governing the movement are more efficient compared to animals that do not show such a side preference, because only one hemisphere is active during the movement if it is lateralized.

It is still unclear what the source of handedness on a population level is. Research with horses and wild chimpanzees suggests a genetic component, but it will take more time to disentangle how and why we observe the right-handedness in human populations. Or wrong-handedness, for the ones who are smudging their ink as they take notes.   



(1)    Neanderthal Lifeways, Subsistence and Technology 2011, Handedness in Neanderthals (pp.139-154)

(2)    J. Comp. Psychol. 2003, 117(4), 371-9

(3)    Laterality 2009, 14(4), 413-22

(4)    J. Comp. Psychol 2015, 129(4), 377-87

(5)    PNAS 2005, 102(35), 12634-38

(6)    Behav. Processes 2008, 79(1), 7-12


Scientists use structural modeling to identify novel opioid analgesics with fewer side effects

Current treatments for chronic pain center on µ-opioid analgesics, such as morphine and oxycodone. These drugs are highly addictive and can fatally depress respiration. The need for new classes of analgesics has been recognized since the 19th century1; however µ-opioid analgesics are still a standard of care in spite of their side effects. As 50 million Americans currently suffer from chronic pain2, innovative treatment options are critical for improved pain management.

A recent study published in Nature by Manglik, Lin, and Aryal et al. 1 used computational  modeling to identify a structurally novel µ-opioid agonist that propagates receptor signaling through the downstream Gi protein (to produce analgesic effects), but does not recruit the downstream β-arrestin-2 protein (thus avoiding certain side effects). To accomplish this, the authors computationally screened over 3 million compounds and rigorously optimized and validated the top hits using additional structural modeling, in vitro opioid receptor binding assays, Gi protein signaling assays, and β-arrestin-2 recruitment assays. PZM21 was identified as a highly selective and potent µ-opiate receptor agonist, which importantly did not show off target effects for hERG or neurotransmitter transporters.

To take the next steps, the authors demonstrated that short-term PZM21 treatment was analgesic and safe in rodents through in vivo behavioral assays. Interestingly, PZM21 produced CNS-specific analgesia in mice, while morphine produced both CNS- and spinal-mediated analgesia. PZM21 was longer-acting and showed fewer side effects than morphine. Importantly, respiratory depression was not observed with PZM21 treatment. Finally, the authors explored the addictive potential of PZM21 in mice. Encouragingly, in hyper-locomotion and conditioned place preference paradigms, PZM21 did not significantly activate dopamine reward circuitry, and thus did not show addictive properties.

I believe this rigorous study creatively bridges computational biology, in vitro biochemistry, and in vivo efficacy models to identify novel alternatives to current analgesics. However, more work is needed to translate PZM21-like molecules to humans. Beyond standard toxicity and dose-finding requirements, I think it will be important to perform addiction studies with extended dosing paradigms past the ten-day window examined here, as chronic pain patients require long-term treatment. Further, as PZM21 is structurally unique from morphine, and therefore may have a different mechanism of action, I think it will be important to assess whether naloxone can reverse PZM21 toxicity for emergency situations.


1) Aashish Manglik*, Henry Lin*, Dipendra K. Aryal*, John D. McCorvy,    Daniela Dengler, Gregory Corder, Anat Levit, Ralf C. Kling, Viachaslau Bernat, Harald Hübner, Xi-Ping Huang, Maria F. Sassano, Patrick M. Giguère, Stefan Löber, Da Duan, Grégory Scherrer, Brian K. Kobilka, Peter Gmeiner, Bryan L. Roth & Brian K. Shoichet. Structure-based discovery of opioid analgesics with reduced side effects Nature 1–6 (2016) doi:10.1038/nature19112

2) NIH Study Shows Prevalence of Chronic or Severe Pain in U.S. Adults. 2016.

Image taken from article


Brains Understanding Computers Understanding Brains

“Computers aren’t smart.”  That’s the first thing my professor said on the first day of in Intro to Computer Science. “They’re dumb, but they’re fast,” he added.  At first I couldn’t believe what my professor was saying.  Computers seem to be quite intelligent.  IBM’s Watson could compete with Jeopardy! champions.  Need to know the answer to a question?  Just type it into Google.  Over the last few years, as I’ve learned more about computer science, I’ve come to learn that what my professor said on that first day of class is absolutely true.
In order to work, computers require extremely specific and detailed instructions laid out in a code they can understand.  Leave out a semicolon at the end of a line?  Forget it.  The computer will stop working.  A computer is nothing without a human brain to help it along.
The real value to a computer, of course, is its speed.  Today, an average laptop can carry out over a billion instructions in just one second.  Need to add up a million numbers in a spreadsheet?  Today’s computers can do so instantly.  Today’s computers can analyze massive amounts of data in very short amounts of time.
This is welcome feature for researchers studying the brain.  A single brain scan today can generate several gigabytes of data.  Even 25 years ago, this was unthinkable [1].  With new projects like the US Government’s BRAIN Initiative, research centers across the country are generating more data on the human brain than ever before [2].  To analyze this data, researchers are working hard to develop new algorithms and computational techniques.  Many scientists have expressed how important it is to train new researchers in the science of “big data” if we are ever going to truly understand how the brain works [3], [4].
The “big data” methods being used to better understand the human brain are the same that determine which advertisements show up in your web browser; the same that help Google decide what you’re searching for; the same that stock brokers use on Wall Street; and the same that the NSA controversially uses to “protect” sensitive American communications.
With big data, computers are starting to look like they might actually be smarter than humans.  But this isn’t true.  Without a human brain to ask the right questions and interpret the results, big data algorithms are worthless.  Rather, humans and computers are beginning to form a symbiotic relationship.  We use computers to speed up our own mental processing.  And now in neuroscience, we use computers and the artificial intelligence we have given them, to better understand our own intelligence, and our own minds.
[3] Sukel, K. “Big Data and the Brain: Peaking at the Future of Neuroscience.”, 8 Dec 2015. Web.
[4] Van Horn, JD. “Opinion: Big data biomedicine offers big higher education opportunities. Proc Natl Acad Sci USA, 7 June 2016:113(23):6322-4 doi: 10.1073/pnas/1607582113.



Brain Connectivity and Video Game Addiction

Pokemon Go, a game that allows users to catch Pokemon while walking around, has taken the world by storm. The app has surpassed Netflix, Twitter, and Tinder in popularity.  The game’s immense success got me curious about video game addiction and the neuroscience behind it.

In a study published in Addiction Biology in December 2015, researchers found that there were differences in brain connectivity in adolescent boys who were compulsive video game players compared to boys without the disorder. fMRI was performed on 106 boys aged 10-19 who were being treated for Internet gaming disorder and 80 boys without the disorder. In those with Internet gaming disorder, brain regions associated with vision and hearing (auditory cortex and frontal eye field) had enhanced connection to the motor cortex and  “salience network”, which focuses attention on important events and response action. Researchers also found increased coordination between the dorsolateral prefrontal cortex and the temporoparietal junction, which has also been seen in patients with neuropsychiatric conditions such as schizophrenia, Down’s syndrome, and autism. The study was a collaboration between Chung-Ang University School of Medicine in South Korea and the University of Utah School of Medicine.

The researchers stated that it’s unknown whether persistent video gaming causes rewiring of the brain or whether individuals with differently wired brains are drawn to video games. While Pokemon Go has benefitted its users by increasing fitness and becoming a social experience, it has also caused accidents due to distracted users. Pokemon Go certainly seems to have gained quite a following. Who knows what an fMRI study could reveal…