October 19, 2022 • 20 mins
#5 — Mike Cianfrocco is Research Assistant Professor at the University of Michigan Life Sciences Institute and Assistant Professor in the Department of Biological Chemistry at the University of Michigan Medical School. In this episode of Cryo-Talk, Mike joins Eva Amsen to talk about the tools he is developing for cryoEM users, such as COSMIC2. He also chats about his love of gardening and fermented foods. Tune in now to hear more!
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Eva Amsen (00:07):
i'm ava Anderson and i'm here today with Mike camp Rocco research assistant professor at the University of Michigan life sciences Institute and assistant professor in the department of biological chemistry at the University of Michigan Medical School.
His research group not only uses cryo em as a tool to study structural biology, but he also developed tools for cryo em and we'll hear a bit more about that in this episode so Mike how are you today.
Michael Cianfrocco (00:33):
Great glad to be here.
Eva Amsen (00:35):
Thanks for joining us so we always ask our guests to start off by telling a little bit about your career, so far, so how did you get to where you are now.
Michael Cianfrocco (00:47):
Well that's a big question I guess.
For me.
I started by at some point during college falling love the protein structure, because I had taken chemistry classes and then by the third year of college.
Taking a bio chemistry class and at that point discovered how enzymes work, and it was amazing that they were connecting.
(01:10):
organic chemistry with biology and protein function, and then I think that, coupled with being a very visual person I was just really drawn to what protein look like and then just.
diving into all the you know high mall and later camera looking at protein structure is always like threw me along and then.
To where I am today as far as criterium I started Grad school, knowing that I wanted to protein structure, but I wasn't sure exactly.
(01:36):
Where i'd fit in, and it was during your rotations in lives, I wrote a with a bunch of dollars his lab and.
I because i'm a visual person I think there's something really satisfying about looking at proteins.
And I even remember when I was in undergrad there even back then not too long ago, you could their images from cryo vm or negative stadium in textbooks of like ATP synthesis or.
(01:57):
different things, and always amazed like that that's what they look like So for me that was always the draw was I.
This is what proteins look like and then it was early days in prior vm but the senegal's lab was just doing really cool work applying it to existing systems like.
Micro tutorials and transcription and that sort of was my lead into it then because doing cryo em you know in 2007 2008.
(02:20):
You had to know Linux and command line which I didn't when I started and I learned on the way.
But then that that became the fodder for sort of future computational development because I was really learned that the nuts and bolts of how a lot of criterium software work.
And then, it was after my PhD that you're looking for a postdoctoral fellowship lab I went to.
(02:42):
Andre schlessinger is live and then samurai peterson's lab and I knew I wanted to keep doing cry Why am I wanted to keep.
Also, becoming really independent inquiry um but then picking up a system, and that was in the REC peterson lab of dining migratory bill trafficking.
And then those became the parallel threads that are in my lab which is we're studying micro to police trafficking with motor proteins and different cargoes and then.
(03:05):
I still like being under the hood inquiry and also helping people use quietly and so that's sort of the two sides my personality that I think if you.
were to meet me and talk to me you'd see that i'm passionate about both and we'll talk about both at length, which I think is sort of maybe surprising to some people.
Eva Amsen (03:21):
yeah yes, I was, I was really interested in them in reading a bit about the computational tools that you're working on, can you tell me a bit about that.
Michael Cianfrocco (03:31):
So the one that i'm that's been working progress for a while that i'm excited to have taking off is this science gateway for cryo em that's called cosmic to.
And so, for people haven't heard of it, yet the idea was we want to connect scientists to supercomputers or to cloud resources without.
any sort of process and headache, and so I discovered when I was a postdoc that Dan SF funds.
(03:55):
Initiatives where they will give you money to build a website that sits on top of a supercomputer.
And so, in the United States, the National Science Foundation funds supercomputers all over the United States and they're freely available, but you have to pay it, you have to apply for time to get on them.
And so the idea was if I wrote a platform and designed the platform, then I applied for them the computing time, then I could share with structural biologists or anyone who wanted to use it.
(04:22):
Because prior to this, I have been tinkering with and dabbling in different kinds of public cloud providers like Amazon web services or Google cloud and they're really powerful nice with their expensive, and so the nsf route meant that it was free.
And so that's what was always appealing is that if we could leverage the the investments that the government has made.
In supercomputers we could help the structural biology community into this web platform oh sorry go ahead.
Eva Amsen (04:47):
yeah, though, so I just wanted to clarify you're basically giving other biologists the chance to use the supercomputer time through the platform.
Michael Cianfrocco (04:55):
yeah exactly, and I think what's nice about it is that it's it I guess i've always found very satisfying to help people.
Do science and learn science and learn structural biology, and so I think this hits at some part of my personality that I just find rewarding is to help.
Put tools in so in general, the whole web platform, none of the software, we wrote I just connect people like I can speak both languages, I think I have.
(05:18):
Enough empathy of the end users who don't understand Linux command lines high performance computers, but I also can talk to the developers to think through.
How to put tools in or what steps can we, you know make easier or things like that.
And so the platform was originally built for cryo em because it was this is my proposed this for years ago it was still coming up now it's much more established but.
(05:42):
Since then a big revolution happened, which has protein structure prediction, and so, most of the jobs run today are actually related to protein structure prediction enough of old.
and cold cold and all those things, and I think that's what we're leaning into as far as helping not now, with no longer just cry oem it's.
Anyone in life sciences research now it's, not even a structural biologist because people are using it.
(06:04):
For protein structure prediction, for you know cell biology labs like chemistry labs everyone is is using it, and so i'm excited to see that taking off and so we're.
Up to around 800 users and almost 7000 jobs submitted, and so it feels it feels satisfying to see that's actually taking off and I can see the sweet spot of where a nice could be for this platform moving forward.
Eva Amsen (06:26):
yeah yeah that kind of brings me to my next question, if you look at what you what you see happening in Korea we am so do you see it being used in different applications now.
Michael Cianfrocco (06:39):
Yes, I mean criterium is being used all over life sciences research for sure, I think the thing that I think about is how do we.
How do we help all those people come into the field and use it without a lot of headaches and I think people will do criteria and there's a lot of.
there's a lot of steps there's still are kind of manual interventions are there sort of things that you might need to learn, and so I think the other side of the algorithm development that.
(07:03):
we've done in my lab is try to capture expertise into expert expertise into machine learning algorithms, and so I think i'm my ideas and those people let's say you have a you know.
I don't know let's say yeast biologist or a plant biologists who they don't don't care about crime, they don't care about for you transforms they do under the structure their protein.
And so, how do you help them do that without you know well, giving them the tools to be rigorous and to interpret things correctly, but also not get into the weeds if you don't want to.
(07:32):
And so I think that's one that's the other side of the algorithm development that we work on, and I think the it's a sometimes it's feels.
Fine, to think about, because if that side of a lab goes super well then, like all of our expertise becomes you know that's I ever expertise is no longer needed.
so that you know, a decade plus of time doing prior vm may become you know not as relevant for single particle which I guess is what progress and technology and usually means.
(07:58):
But that's it's it's it's a funny project, especially you know if you're trying to engineer yourself out of something if you work, what happens next.
Eva Amsen (08:07):
And, and in the in the over the course of your research have you ever as there ever been a moment where things took a surprising turn.
Michael Cianfrocco (08:17):
yeah I mean, I think the probably one of the bigger ones was during my PhD project when the golf club, where we are working on this transcription factor complex, and this is before we had directors and all the fancy things that we have today.
But we still could see the protein that I wanted to study, which was a transcription factor called you have to do we knew DNA was binding to it, we couldn't figure out how and.
(08:38):
It was at a Gordon conference at a small research conference when I was just you're redoing a bunch of like kind of structural biology controls and recalculating reconstructions.
That I was looking at the data and realize that actually had essentially I summarized in a weird way where a piece had moved.
Like 200 extra hours away from where it was before, but it's still look like a V shape and so it was confusing because it looks like in both states there a V, if you look really carefully, you realize that things got swapped.
(09:06):
And so that was the to me that was a really satisfying experience discovery, because it explained my PhD workers very helpful but also was like.
I could go back to data, before I was in the lab and see it there, so it was one of the things really your eyes got open, then you could see it everywhere, and it was it was always there and so it was really satisfying to be.
going to previous data sets I never had touched I can look at it in and find it all there, so it was like a really satisfying discovery in that respect to.
Eva Amsen (09:31):
yeah that's amazing when that happens, I think, so that we make sense and.
Michael Cianfrocco (09:34):
yeah I was also really worried that like when I was.
When I graduated that like no one was going to be able to repeat it, so I was also satisfied to see all the work that nikolas live has done to to see and really define the mechanism from the sort of very low resolution blobs and, most of us have tried to see that I wasn't totally wrong.
Eva Amsen (09:52):
that's always good to know.
yeah um So what are you looking forward to in your career over the next few years.
Michael Cianfrocco (10:00):
I mean, I guess it depends on which search hat i'm wearing, and so I guess to answer a few different ways the guess, who would have thought, what computational parts of cry oh yeah I mean I think i'm interested in.
Thinking about how we can use the revolutions in Ai and machine learning for cryo em but with respect to either automating prior vm.
or automating analysis routines and cryo am so that way, we can feel good about the results we get there also faster that's experts cruise through data sets much more easily.
(10:28):
And that's new users come in and not have to worry about getting in the weeds so excited about that, I think, for the cosmic to platform for sort of delivering cloud computing.
I think it's leaning into all of the protein structure prediction, and then on the heels of protein structure prediction is all the protein design and.
other kind of structural biology analysis tools, I think, helping life sciences life scientists, researchers use those via this platform is one of my visions for myself.
(10:57):
Along those lines of be bridging into tomography for the platform so definitely all the crowd demographers have.
Even more of a competition or hurdle than single particle cryo em and so trying to support that Community as well with tools.
For the biology side, I mean i'm honestly to introduce where we're studying we're trying to study cargo trafficking with motor proteins on microbial so can he sins and dining.
(11:19):
And i'm excited to try and reconstitute different types of car goes with motor proteins to figure out how it's regulated, and so we have a few different projects that are.
sort of working on right now but i'm that's what i'm excited to see go, especially with.
respect to connecting tomography to just but in vitro purified protein so very complicated in vitro systems, but doing tomography and looking at structures of motors on cargo is is also where i'm excited to go.
Eva Amsen (11:45):
ooh sounds exciting and what do you do when you're not working.
Michael Cianfrocco (11:52):
Well, these days, I mean I have, I have two little kids that i'm with every day and they're great.
When i'm not with them i'm we have a house and an arbor and we have lots of gardens that we take care of and so the hobby is.
Vegetable gardening in growing food and cooking food, and you know which in between the lines, is that a lot of reading right gardening essentially weed weed control reading.
(12:14):
But yeah excited will say to do all different kinds of tomatoes peppers vegetables, you know we like eating food and cooking and also growing.
Eva Amsen (12:22):
So what if you made with food from your own garden.
Michael Cianfrocco (12:26):
of love lots of things, I mean I think my my lab I think would like me to bring more of the tomatoes and peppers that I grow in by end up you know, preserving it all in like either.
Danny it or freezing it and cooking it because we're excited to just keep it and use it into the winter so either it's hot sauces from the peppers or tomato sauces or things from the tomatoes and salads like everything there's a lot of different vegetables.
Eva Amsen (12:53):
i've been growing tomatoes, but they're not turning red and I recently found out it's because we've had a heatwave in the UK and that's like stopping them from turning red.
Really frustrating.
Michael Cianfrocco (13:06):
just watching and waiting.
yeah yeah.
Eva Amsen (13:09):
it's done just the wrong color.
Michael Cianfrocco (13:12):
Sometimes I also imagine that the gardening that we do is like what you also would expect from like an assistant professors garden where it's like a lot of ideas really excited.
A lot of things happening that i'm sure will be pruned down as we move on there, as you come to grips with reality, but there's a lot of a lot of things happening.
Eva Amsen (13:29):
And, and do you prefer the countryside or the city.
Michael Cianfrocco (13:35):
I mean we the House, we live in, we didn't imagine that we would be living here when we moved in over five years ago we were mostly had to.
find a place to live in short time because we were expecting our second child, and so we.
were looking to live closer to the campus but then lived outside of run the outskirts and we've mostly been leaning into that now we don't know how long will live.
(13:56):
Like with such space we never had this much space in our life and so it's just been you know leading into that and having lots of things growing everywhere.
Eva Amsen (14:05):
And, and what What else do you do, do you like reading, do you have any book recommendations.
Michael Cianfrocco (14:11):
Reading I feel like I used to read a lot more when I was in Grad school and as a postdoc and I feel like i've read less the book that I picked up recently that i've really been enjoying is called the over story.
And it's when I think wonderful surprise last year, but it's kind of just fiction about thinking about trees and nature but interfacing with these short stories of people so it's been really nice book to read.
Eva Amsen (14:36):
it's funny that you mentioned reading less because that's exactly what Liz keylock said in the previous episode I.
Just recorded.
Like you get too busy with science and you get less time to read.
Michael Cianfrocco (14:48):
You that's not good, I think it'd be better to be reading more because it makes me better writer better communicator more thinking outside of the world, I live in which would be panicking better at it.
Eva Amsen (15:00):
And Have you had any time to watch films or TV and anything you can recommend or enjoy this past year.
Michael Cianfrocco (15:08):
I don't know I guess yeah I feel like we watch different TV shows.
I mean film, the one that stands out that I just saw was the everything everywhere all at once, which was really cool I really liked it I didn't know.
How I would like i'm using someone who likes more fantasy but it's like this interesting take on fantasy and relationships and family dynamics that was really I really liked it.
Eva Amsen (15:34):
And do you like music.
Michael Cianfrocco (15:36):
yeah for for music it it varies a lot but yeah I listened to a lot of different music, probably the one that people are usually surprised about is, I have been on kind of a metal.
streak for the last few years, and so that's the like go to choice these days.
Eva Amsen (15:53):
Is that like what you listen to, while you work or.
Michael Cianfrocco (15:56):
Just depends like.
Eva Amsen (15:58):
we're driving.
Michael Cianfrocco (15:59):
yeah I mean working in the garden, and all this or that or you know when I have a lucky chance to use the microscopes myself, which I still do sometimes.
it's usually the same that kind of music, which is.
What I just like it.
Eva Amsen (16:13):
yeah that's what I one of my memories for my lab days is just being alone in the microscope room and the weekend and playing whatever music, I wanted to hear.
Michael Cianfrocco (16:22):
yeah exactly I don't get to be in the microscope that much, but I still feel like I still get chance to get on it and to tinker or to try and you know try different data collection approaches which is usually really satisfying to do.
Eva Amsen (16:36):
And this is always a question I like asking people if you were not a scientist, what would you be.
Michael Cianfrocco (16:43):
It would definitely be something with food and cooking and the if there's one type of cooking that I like it's fermenting foods.
So either sauerkraut or kimchi cheese or beer or anything hard cider, so I think it'd be something related to that and also.
Also really coffee, and so we roast coffee at my house as well, so something with it the interface of food and I guess also service, I think.
(17:10):
Maybe it's related to hosting this web server I don't mind I like helping people, and so, sharing good food or good drink would also be something i'd like to do.
Eva Amsen (17:19):
sounds like you be running an amazing CAFE.
Michael Cianfrocco (17:23):
And you could do some criteria in the background.
Eva Amsen (17:25):
yeah Riley and CAFE.
And, and so, finally, do you have any advice for researchers who are just starting out.
Michael Cianfrocco (17:37):
I mean it's a great time to be doing sociobiology especially post alpha fold I think structural biology is is the future, I think the the advice I would have now is.
Finding where can you find connections between experimental structural biology like single particle X Ray crowd tomography with sort of protein structure modeling and design.
(17:58):
And so, where, can you make a loop and also where, could you make that loop yourself like what projects, could you do, where you could iterate without having to collaborate.
In a really far away with either some very complicated experiment, or some very huge.
machine learning project it's where can you work you find the interface, but I think sitting at the interface of those two.
would be really exciting and I think the future is going back and forth a lot, I think, running cosmic to means we've installed and run we run our fold and.
(18:25):
All this room protein structure prediction programs and installing and writing them made me realize how as like an experimental cry we have structural biologist.
I don't a lot of my depth when it comes to sort of the bioinformatics you know sequence alignments and all these things that i'm not an expert at.
And so I feel like being able to do both.
Is feels like a future or structural biology, especially as we think about protein variants mutations post relational modifications.
(18:50):
Protein confrontational changes if you can be able to relate let's say experimental chromium structure that you get.
Back to some protein energetic state to understand whether or not you would expect the protein to work like this, I feel like that would be that'd be really powerful and I that's something that I have my eye on for sure.
Eva Amsen (19:08):
that's some really good, practical advice thanks um so that brings us to the end of our episode today, so thank you Mike for joining us.
Michael Cianfrocco (19:19):
thanks for having me yeah.
Eva Amsen (19:20):
And Thank you everyone for listening to or watching cryo talk.
i'm ava Anderson and i'm here today with Mike camp Rocco research assistant professor at the University of Michigan life sciences Institute and assistant professor in the department of biological chemistry at the University of Michigan Medical School.
His research group not only uses cryo em as a tool to study structural biology, but he also developed tools for cryo em and we'll hear a bit more about that in this episode so Mike how are you today.
Michael Cianfrocco (00:33):
Great glad to be here.
Eva Amsen (00:35):
Thanks for joining us so we always ask our guests to start off by telling a little bit about your career, so far, so how did you get to where you are now.
Michael Cianfrocco (00:47):
Well that's a big question I guess.
For me.
I started by at some point during college falling love the protein structure, because I had taken chemistry classes and then by the third year of college.
Taking a bio chemistry class and at that point discovered how enzymes work, and it was amazing that they were connecting.
(01:10):
organic chemistry with biology and protein function, and then I think that, coupled with being a very visual person I was just really drawn to what protein look like and then just.
diving into all the you know high mall and later camera looking at protein structure is always like threw me along and then.
To where I am today as far as criterium I started Grad school, knowing that I wanted to protein structure, but I wasn't sure exactly.
(01:36):
Where i'd fit in, and it was during your rotations in lives, I wrote a with a bunch of dollars his lab and.
I because i'm a visual person I think there's something really satisfying about looking at proteins.
And I even remember when I was in undergrad there even back then not too long ago, you could their images from cryo vm or negative stadium in textbooks of like ATP synthesis or.
(01:57):
different things, and always amazed like that that's what they look like So for me that was always the draw was I.
This is what proteins look like and then it was early days in prior vm but the senegal's lab was just doing really cool work applying it to existing systems like.
Micro tutorials and transcription and that sort of was my lead into it then because doing cryo em you know in 2007 2008.
(02:20):
You had to know Linux and command line which I didn't when I started and I learned on the way.
But then that that became the fodder for sort of future computational development because I was really learned that the nuts and bolts of how a lot of criterium software work.
And then, it was after my PhD that you're looking for a postdoctoral fellowship lab I went to.
(02:42):
Andre schlessinger is live and then samurai peterson's lab and I knew I wanted to keep doing cry Why am I wanted to keep.
Also, becoming really independent inquiry um but then picking up a system, and that was in the REC peterson lab of dining migratory bill trafficking.
And then those became the parallel threads that are in my lab which is we're studying micro to police trafficking with motor proteins and different cargoes and then.
(03:05):
I still like being under the hood inquiry and also helping people use quietly and so that's sort of the two sides my personality that I think if you.
were to meet me and talk to me you'd see that i'm passionate about both and we'll talk about both at length, which I think is sort of maybe surprising to some people.
Eva Amsen (03:21):
yeah yes, I was, I was really interested in them in reading a bit about the computational tools that you're working on, can you tell me a bit about that.
Michael Cianfrocco (03:31):
So the one that i'm that's been working progress for a while that i'm excited to have taking off is this science gateway for cryo em that's called cosmic to.
And so, for people haven't heard of it, yet the idea was we want to connect scientists to supercomputers or to cloud resources without.
any sort of process and headache, and so I discovered when I was a postdoc that Dan SF funds.
(03:55):
Initiatives where they will give you money to build a website that sits on top of a supercomputer.
And so, in the United States, the National Science Foundation funds supercomputers all over the United States and they're freely available, but you have to pay it, you have to apply for time to get on them.
And so the idea was if I wrote a platform and designed the platform, then I applied for them the computing time, then I could share with structural biologists or anyone who wanted to use it.
(04:22):
Because prior to this, I have been tinkering with and dabbling in different kinds of public cloud providers like Amazon web services or Google cloud and they're really powerful nice with their expensive, and so the nsf route meant that it was free.
And so that's what was always appealing is that if we could leverage the the investments that the government has made.
In supercomputers we could help the structural biology community into this web platform oh sorry go ahead.
Eva Amsen (04:47):
yeah, though, so I just wanted to clarify you're basically giving other biologists the chance to use the supercomputer time through the platform.
Michael Cianfrocco (04:55):
yeah exactly, and I think what's nice about it is that it's it I guess i've always found very satisfying to help people.
Do science and learn science and learn structural biology, and so I think this hits at some part of my personality that I just find rewarding is to help.
Put tools in so in general, the whole web platform, none of the software, we wrote I just connect people like I can speak both languages, I think I have.
(05:18):
Enough empathy of the end users who don't understand Linux command lines high performance computers, but I also can talk to the developers to think through.
How to put tools in or what steps can we, you know make easier or things like that.
And so the platform was originally built for cryo em because it was this is my proposed this for years ago it was still coming up now it's much more established but.
(05:42):
Since then a big revolution happened, which has protein structure prediction, and so, most of the jobs run today are actually related to protein structure prediction enough of old.
and cold cold and all those things, and I think that's what we're leaning into as far as helping not now, with no longer just cry oem it's.
Anyone in life sciences research now it's, not even a structural biologist because people are using it.
(06:04):
For protein structure prediction, for you know cell biology labs like chemistry labs everyone is is using it, and so i'm excited to see that taking off and so we're.
Up to around 800 users and almost 7000 jobs submitted, and so it feels it feels satisfying to see that's actually taking off and I can see the sweet spot of where a nice could be for this platform moving forward.
Eva Amsen (06:26):
yeah yeah that kind of brings me to my next question, if you look at what you what you see happening in Korea we am so do you see it being used in different applications now.
Michael Cianfrocco (06:39):
Yes, I mean criterium is being used all over life sciences research for sure, I think the thing that I think about is how do we.
How do we help all those people come into the field and use it without a lot of headaches and I think people will do criteria and there's a lot of.
there's a lot of steps there's still are kind of manual interventions are there sort of things that you might need to learn, and so I think the other side of the algorithm development that.
(07:03):
we've done in my lab is try to capture expertise into expert expertise into machine learning algorithms, and so I think i'm my ideas and those people let's say you have a you know.
I don't know let's say yeast biologist or a plant biologists who they don't don't care about crime, they don't care about for you transforms they do under the structure their protein.
And so, how do you help them do that without you know well, giving them the tools to be rigorous and to interpret things correctly, but also not get into the weeds if you don't want to.
(07:32):
And so I think that's one that's the other side of the algorithm development that we work on, and I think the it's a sometimes it's feels.
Fine, to think about, because if that side of a lab goes super well then, like all of our expertise becomes you know that's I ever expertise is no longer needed.
so that you know, a decade plus of time doing prior vm may become you know not as relevant for single particle which I guess is what progress and technology and usually means.
(07:58):
But that's it's it's it's a funny project, especially you know if you're trying to engineer yourself out of something if you work, what happens next.
Eva Amsen (08:07):
And, and in the in the over the course of your research have you ever as there ever been a moment where things took a surprising turn.
Michael Cianfrocco (08:17):
yeah I mean, I think the probably one of the bigger ones was during my PhD project when the golf club, where we are working on this transcription factor complex, and this is before we had directors and all the fancy things that we have today.
But we still could see the protein that I wanted to study, which was a transcription factor called you have to do we knew DNA was binding to it, we couldn't figure out how and.
(08:38):
It was at a Gordon conference at a small research conference when I was just you're redoing a bunch of like kind of structural biology controls and recalculating reconstructions.
That I was looking at the data and realize that actually had essentially I summarized in a weird way where a piece had moved.
Like 200 extra hours away from where it was before, but it's still look like a V shape and so it was confusing because it looks like in both states there a V, if you look really carefully, you realize that things got swapped.
(09:06):
And so that was the to me that was a really satisfying experience discovery, because it explained my PhD workers very helpful but also was like.
I could go back to data, before I was in the lab and see it there, so it was one of the things really your eyes got open, then you could see it everywhere, and it was it was always there and so it was really satisfying to be.
going to previous data sets I never had touched I can look at it in and find it all there, so it was like a really satisfying discovery in that respect to.
Eva Amsen (09:31):
yeah that's amazing when that happens, I think, so that we make sense and.
Michael Cianfrocco (09:34):
yeah I was also really worried that like when I was.
When I graduated that like no one was going to be able to repeat it, so I was also satisfied to see all the work that nikolas live has done to to see and really define the mechanism from the sort of very low resolution blobs and, most of us have tried to see that I wasn't totally wrong.
Eva Amsen (09:52):
that's always good to know.
yeah um So what are you looking forward to in your career over the next few years.
Michael Cianfrocco (10:00):
I mean, I guess it depends on which search hat i'm wearing, and so I guess to answer a few different ways the guess, who would have thought, what computational parts of cry oh yeah I mean I think i'm interested in.
Thinking about how we can use the revolutions in Ai and machine learning for cryo em but with respect to either automating prior vm.
or automating analysis routines and cryo am so that way, we can feel good about the results we get there also faster that's experts cruise through data sets much more easily.
(10:28):
And that's new users come in and not have to worry about getting in the weeds so excited about that, I think, for the cosmic to platform for sort of delivering cloud computing.
I think it's leaning into all of the protein structure prediction, and then on the heels of protein structure prediction is all the protein design and.
other kind of structural biology analysis tools, I think, helping life sciences life scientists, researchers use those via this platform is one of my visions for myself.
(10:57):
Along those lines of be bridging into tomography for the platform so definitely all the crowd demographers have.
Even more of a competition or hurdle than single particle cryo em and so trying to support that Community as well with tools.
For the biology side, I mean i'm honestly to introduce where we're studying we're trying to study cargo trafficking with motor proteins on microbial so can he sins and dining.
(11:19):
And i'm excited to try and reconstitute different types of car goes with motor proteins to figure out how it's regulated, and so we have a few different projects that are.
sort of working on right now but i'm that's what i'm excited to see go, especially with.
respect to connecting tomography to just but in vitro purified protein so very complicated in vitro systems, but doing tomography and looking at structures of motors on cargo is is also where i'm excited to go.
Eva Amsen (11:45):
ooh sounds exciting and what do you do when you're not working.
Michael Cianfrocco (11:52):
Well, these days, I mean I have, I have two little kids that i'm with every day and they're great.
When i'm not with them i'm we have a house and an arbor and we have lots of gardens that we take care of and so the hobby is.
Vegetable gardening in growing food and cooking food, and you know which in between the lines, is that a lot of reading right gardening essentially weed weed control reading.
(12:14):
But yeah excited will say to do all different kinds of tomatoes peppers vegetables, you know we like eating food and cooking and also growing.
Eva Amsen (12:22):
So what if you made with food from your own garden.
Michael Cianfrocco (12:26):
of love lots of things, I mean I think my my lab I think would like me to bring more of the tomatoes and peppers that I grow in by end up you know, preserving it all in like either.
Danny it or freezing it and cooking it because we're excited to just keep it and use it into the winter so either it's hot sauces from the peppers or tomato sauces or things from the tomatoes and salads like everything there's a lot of different vegetables.
Eva Amsen (12:53):
i've been growing tomatoes, but they're not turning red and I recently found out it's because we've had a heatwave in the UK and that's like stopping them from turning red.
Really frustrating.
Michael Cianfrocco (13:06):
just watching and waiting.
yeah yeah.
Eva Amsen (13:09):
it's done just the wrong color.
Michael Cianfrocco (13:12):
Sometimes I also imagine that the gardening that we do is like what you also would expect from like an assistant professors garden where it's like a lot of ideas really excited.
A lot of things happening that i'm sure will be pruned down as we move on there, as you come to grips with reality, but there's a lot of a lot of things happening.
Eva Amsen (13:29):
And, and do you prefer the countryside or the city.
Michael Cianfrocco (13:35):
I mean we the House, we live in, we didn't imagine that we would be living here when we moved in over five years ago we were mostly had to.
find a place to live in short time because we were expecting our second child, and so we.
were looking to live closer to the campus but then lived outside of run the outskirts and we've mostly been leaning into that now we don't know how long will live.
(13:56):
Like with such space we never had this much space in our life and so it's just been you know leading into that and having lots of things growing everywhere.
Eva Amsen (14:05):
And, and what What else do you do, do you like reading, do you have any book recommendations.
Michael Cianfrocco (14:11):
Reading I feel like I used to read a lot more when I was in Grad school and as a postdoc and I feel like i've read less the book that I picked up recently that i've really been enjoying is called the over story.
And it's when I think wonderful surprise last year, but it's kind of just fiction about thinking about trees and nature but interfacing with these short stories of people so it's been really nice book to read.
Eva Amsen (14:36):
it's funny that you mentioned reading less because that's exactly what Liz keylock said in the previous episode I.
Just recorded.
Like you get too busy with science and you get less time to read.
Michael Cianfrocco (14:48):
You that's not good, I think it'd be better to be reading more because it makes me better writer better communicator more thinking outside of the world, I live in which would be panicking better at it.
Eva Amsen (15:00):
And Have you had any time to watch films or TV and anything you can recommend or enjoy this past year.
Michael Cianfrocco (15:08):
I don't know I guess yeah I feel like we watch different TV shows.
I mean film, the one that stands out that I just saw was the everything everywhere all at once, which was really cool I really liked it I didn't know.
How I would like i'm using someone who likes more fantasy but it's like this interesting take on fantasy and relationships and family dynamics that was really I really liked it.
Eva Amsen (15:34):
And do you like music.
Michael Cianfrocco (15:36):
yeah for for music it it varies a lot but yeah I listened to a lot of different music, probably the one that people are usually surprised about is, I have been on kind of a metal.
streak for the last few years, and so that's the like go to choice these days.
Eva Amsen (15:53):
Is that like what you listen to, while you work or.
Michael Cianfrocco (15:56):
Just depends like.
Eva Amsen (15:58):
we're driving.
Michael Cianfrocco (15:59):
yeah I mean working in the garden, and all this or that or you know when I have a lucky chance to use the microscopes myself, which I still do sometimes.
it's usually the same that kind of music, which is.
What I just like it.
Eva Amsen (16:13):
yeah that's what I one of my memories for my lab days is just being alone in the microscope room and the weekend and playing whatever music, I wanted to hear.
Michael Cianfrocco (16:22):
yeah exactly I don't get to be in the microscope that much, but I still feel like I still get chance to get on it and to tinker or to try and you know try different data collection approaches which is usually really satisfying to do.
Eva Amsen (16:36):
And this is always a question I like asking people if you were not a scientist, what would you be.
Michael Cianfrocco (16:43):
It would definitely be something with food and cooking and the if there's one type of cooking that I like it's fermenting foods.
So either sauerkraut or kimchi cheese or beer or anything hard cider, so I think it'd be something related to that and also.
Also really coffee, and so we roast coffee at my house as well, so something with it the interface of food and I guess also service, I think.
(17:10):
Maybe it's related to hosting this web server I don't mind I like helping people, and so, sharing good food or good drink would also be something i'd like to do.
Eva Amsen (17:19):
sounds like you be running an amazing CAFE.
Michael Cianfrocco (17:23):
And you could do some criteria in the background.
Eva Amsen (17:25):
yeah Riley and CAFE.
And, and so, finally, do you have any advice for researchers who are just starting out.
Michael Cianfrocco (17:37):
I mean it's a great time to be doing sociobiology especially post alpha fold I think structural biology is is the future, I think the the advice I would have now is.
Finding where can you find connections between experimental structural biology like single particle X Ray crowd tomography with sort of protein structure modeling and design.
(17:58):
And so, where, can you make a loop and also where, could you make that loop yourself like what projects, could you do, where you could iterate without having to collaborate.
In a really far away with either some very complicated experiment, or some very huge.
machine learning project it's where can you work you find the interface, but I think sitting at the interface of those two.
would be really exciting and I think the future is going back and forth a lot, I think, running cosmic to means we've installed and run we run our fold and.
(18:25):
All this room protein structure prediction programs and installing and writing them made me realize how as like an experimental cry we have structural biologist.
I don't a lot of my depth when it comes to sort of the bioinformatics you know sequence alignments and all these things that i'm not an expert at.
And so I feel like being able to do both.
Is feels like a future or structural biology, especially as we think about protein variants mutations post relational modifications.
(18:50):
Protein confrontational changes if you can be able to relate let's say experimental chromium structure that you get.
Back to some protein energetic state to understand whether or not you would expect the protein to work like this, I feel like that would be that'd be really powerful and I that's something that I have my eye on for sure.
Eva Amsen (19:08):
that's some really good, practical advice thanks um so that brings us to the end of our episode today, so thank you Mike for joining us.
Michael Cianfrocco (19:19):
thanks for having me yeah.
Eva Amsen (19:20):
And Thank you everyone for listening to or watching cryo talk.
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