Science Shoutout Uncategorized

Why should I read this?

Every January MIT offers its students a very underrated short course. The course consists of only one 60 minutes lecture and has no exam at the end. The course name is “How to speak” and professor Patrick Winston was giving it every year for 40 years. All of the concepts and “rules” of speaking that he would talk about in this lecture were already applied in it, giving us a direct example of how science should be communicated.

Before we dive deeper in this topic, maybe we should first clarify why is this even an important topic?

Science 50 years ago and science today are not the same. In the technically advanced era of 21. century it’s hard to keep up with every new breakthrough. I am sure many of you have already experienced this if you’ve spent 3 seconds in research. Stepping in the new field as a master student or a fresh PhD student can be confusing. There are all of these papers already done on every single topic that comes to your mind and as you try filtering them out in order to catch the pace and set the fundamentals for your own research, there are already dozen new papers published in the meantime. The process is exhausting and demotivating. However, if you think about it, often times you spend more time going through a bunch of papers that badly written or that have shady experiments and oversold results, than actually reading the papers you will use as guidance through your own ideas. The explosion in academic papers is the reality we are facing currently and a big part of the reason is the academic pressure put on everyone who enters it. Whatever you do, you must publish. You are valued by how much (rarely also how well) you publish and you are evaluated by the number of citations you have. But okay, let me stop right there because that’s a completely different problem than the one I want to talk about today.

Have you ever found yourself reading a paper form 1970’ where a hard mathematical concept is being explained, but somehow you understood everything (or at least more than you expected)? I have, multiple times. I started noticing that the authors of older papers used much simpler language, they conveyed the ideas in a comprehensive way without over-the-top statements and fancy words that no one understands. Somewhere along the way of expanding science and technology we obviously forgot how to talk about it, how to teach it and how to write about it. And THAT’S what I want to talk about today.

Math really is all around us

Going back to prof. Winston and his lecture from the beginning, in the youtube video description of his last recorded lecture it says “the talk is intended to improve your speaking ability in critical situations by teaching you a few heuristic rules”. Prof. Winston was a computer scientist and somehow he managed to connect speaking with math – which is also one of the thing he talks about in the lecture, how to express your own ideas and passion about the topic you are talking about. I won’t go into too many details of his talk, simply because I truly believe everyone should watch it (or better listen to it), but I will give you a short overview of the main presented ideas. His talk is divided in few sections in the exact ways that he teaches us to do. He talks about how to start a conversation or a presentation and gives a few examples of what to do and what to avoid at the beginning. The most intriguing advice for me in this section was: Do not start a talk with a joke.

He continues to talk about the importance of time and place of your talk, which might seem like thing you already know, but he offers a few interesting points that are very memorable. Then he goes on about the tools that you should use if you are teaching. Also something we all heard about in the school at some point, but even our own teacher were usually bad at implementing those rules in their lectures. Later he talks about how to speak in situations other than teaching (for example at work) and he finishes with a beautiful summary of how to compile all of this into a beautiful informative talk. Oh yes, and a brief note on how to get famous!

The opening sentence of his talk is: “The Uniform Code of Military Justice specifies court material for any officer who sends a soldier into a battle without a weapon. There ought to be a similar protection for students, because students shouldn’t be able to go out into life without the ability to communicate. And that’s because your success in life will be determined largely by your ability to speak, your ability to write and the quality of your ideas, in that order.”

If this didn’t hook you, nothing will. Click here!

Why negotiation is not only important for getting a raise

In the light of prof. Watsons statement about the value of your ability to talk, we will give another good resource for that. At the beginning of this post I also mentioned that in today’s world in general (not just in the academia), you are worth as much as your name is. How you present yourself, the way you talk (or tweet) about your ideas or products or beliefs is 80% of your success. There are many courses out there about how to talk to people, how to be persuasive, how to negotiate etc. and the thing I find most frustrating about them is that they are all focused on getting something with your newly acquired skills – a discount, a raise, a romantic relationship,… Why don’t we teach people how to negotiate and how to interact with others simply in order to better present our emotions or our ideas? Eventually, I found this masterclass on the art of negotiation. The lecturer is Chris Voss, a well known FBI hostage negotiator who persuaded terrorists, bank robbers and kidnappers to see things his way. Although still mostly focused on how to get better deals in life in general, Chris touches upon the topic of communication from the position of a teacher, and he presents some very interesting ideas with clear examples. For those of you who like to read, he has a great book (“Never Split the Difference: Negotiating As If Your Life Depended On It) in which he mostly explains the same concepts and methods as in the masterclass.

Why do I think a good scientist and a good teacher must also be a good negotiator? Because you always need to sell your ideas. Teaching a class on support vector machines? You have to sell them to your students. Talking with your friends about the effects of sea temperature on the marine life? You need to sell it. Otherwise: Click here!

The art of writing

As mentioned, in the ocean of articles that get published monthly, it’s hard enough finding the one that’s relevant to you, let alone being lucky enough to find the one that is also very well written. Personally, I have come across so many badly written papers with great ideas that I started asking myself: how can I make sure that my papers and my presentations don’t end up like that?

This got me searching for some kind of “recipe” for a good article, the do’s and don’ts of writing. One of the best resources that my supervisor actually shared with me is the post titled “ Recipe for a Quality Scientific Paper: Fulfill Readers’ and Reviewers’ Expectations” posted on the website of Sparks Laboratory of structural bioinformatics. The author, prof. Yaoqi Zhou already in the first paragraph makes a very important point: good writing is hard to achieve, but good writing in English when English is not your native language is even harder. Therefore, having a little cheat sheet comes handy, especially when you are just starting your scientific career.

I won’t spoil the contents of this post, especially since I think it’s important for everyone embarking on a research or teaching career to read the entire post, but I will briefly summarize the main parts.

The post is divided into two big sections: how to satisfy the readers and the reviewers and how construct the paper itself.  It goes into concise explanations and examples of how to construct good sentences, paragraphs, and figures for the readers and, on the other hand, how to live up to the expectations of the reviewers. The second part is perhaps more practically useful, as it goes into the details of every section of the paper – introduction, methods, results, discussion, and summary. Every advice is backed up with a very good example from which you can directly learn how to implement author’s suggestions.

Prof. Zhou promises that “The purpose of this article is to enable you to better advertise your results through clarity and convincing evidence…. Let the facts speak for themselves.”, and he delivers. Click here!

Repetitio est mater studiorum!

For those of you who are more prone to listening to the lectures and solving exercises, there is a beautifully designed course on Coursera titled “Writing in the Sciences” which essentially goes through more or less the same concepts that prof. Zhou talks about, but in much more detail with much more examples and with many ways for you to directly practice what you’ve just learnt. One of the great things about this course is that the last two weeks of the course are dedicated to other forms of writing – how to write grant proposals, review papers, letters of recommendation, and how to talk to media, how to be interviewed and what approach to take when talking to a broader audience.

I strongly recommend this course for everyone, not only those of you who need it to write better papers, but to improve your writing in general. It is easy to follow, very comprehensive, fun, doesn’t take a lot of your time and, most importantly – it’s free. Click here!

A little extra

Conference poster – a new level of headache. For those of you who stuck around until the end, I would like to offer you a new outlook on how to make scientific posters that people will actually notice at the science conferences. Lately, more and more posters have been constructed this way, and I can say from my personal experience that this really works! The author of the video put a lot of effort in creating a fun video so without further ado: Click me!

Uncategorized Understanding Science

F for Folding or F for Fuzzy?

In one of our recent posts, we touched upon the intrinsically disordered proteins and regions (IDPs/IDRs, I will use IDP abbreviation in further text). There we explained how their specific primary structure and the choice of very hydrophilic, charged, and small amino acids enables them to exist in an extended conformation thereby defying one of the main paradigms of molecular biology – in order for a protein to be functional, it needs to exist in the most stable fold (called also the native fold). Today we know that not to always be the case, as we have characterized many functions of IDPs, even though they lack globular structure.

Since IDPs exist in this extended, stretched conformation with no distinct secondary or tertiary structure, a huge portion of their surface is exposed to the solvent, meaning that IDPs posses a high entropic energy. However, as soon as they bind to their target protein (and thereby form weak intermolecular bonds and interactions), their entropic energy decreases following the decrease in their exposed surface. As always, nature loves the equilibrium, so this drop in the entropy can’t be ignored – we need to find a way to compensate for this loss so that the overall energy of the system remains more or less the same. IDPs realized that if they follow up this entropy loss with some enthalpy gain, things could work out and nature won’t be forced to abandon IDPs. Once bound, IDPs start forming some sort of a secondary structure by creating intermolecular bonds and interactions. So the entropy that we lost because of the weak intermolecular bonds that were created between the IDP and the target protein is being balanced with the enthalpy gain due to the formation of intramolecular bonds within the IDP. This process, in which IDPs exit as completely disordered and then become ordered when bound to the target protein is called folding upon binding and it’s one of the coolest mechanisms in biochemistry (in my humble opinion at least).

Life is all about balance

In most cases, folding upon binding is energetically favorable for the majority of IDPs, but not everything is as simple as we would want it to be. If the entropic penalty (or decrease) is well compensated with enthalpic gain (in translation: if enough non-covalent bonds are created within the IDP and between IDP and target protein), a strong binding with a high affinity is achieved. However, since only basic secondary structure is being induced in the majority of IDPs (for example simple alpha-helix or beta strand), the number of those newly formed interactions is theoretically limited, meaning that the enthalpic contributions are usually not enough to counteract the entropic penalty and therefore the binding is usually of lower affinity. This is where the story gets messy, but don’t worry, nature has a few tricks up its sleeve…

Firstly, not all IDPs fold upon binding. Even though we said that folding is energetically more favorable than having the highly exposed surface of the extended IDP conformation, this would be the case in a perfect scenario where the gain in enthalpy is sufficient to cover for the loss of entropy. As explained, those newly formed bonds are usually not that strong and therefore we can’t always count that the energies will end add up in the end. Because of this a substantial amount of IDPs retain their disorder even when bound to the binding partner and they never really fold. We call those are “fuzzy” IDPs and they create “fuzzy” complexes with their binding partners where the “fuzziness” depends on the degree of disorder that IDP retains, which is also based upon the relative entropy inherent to the complex (the fuzziness helps to minimize the entropic penalty caused by binding of IDP and the binding partner, which would otherwise be too high to compensate with the increase in enthalpic energy of a newly formed complex, in which case there would be no binding whatsoever). The main characteristic of fuzzy complexes is that the interactions between the IDP and the binding partner protein are transient and coupled with a weak binding affinity (they come and go fast).

Ways to feel fuzzy

The fuzziness can be divided into 4 classes. It’s important to note that they are not mutually exclusive and the disorder at the bound state is a continuum, meaning that at any point in time a protein complex may exhibit the character of more than one class. 

  1. Polymorphic model : In this case the IDP adopts distinct well-defined extended conformations in the bound state, while remaining fully disordered. The 3D conformations of IDP and the protein that are a part of this fuzzy complex are unrelated, meaning that we can’t find traces of lock-key binding mechanism or induced-fit mechanism here.
Figure1: Representation of a polymorphic fuzzy complex (b),(c),(d) here IDP adopts a few conformations when bound to the binding partner. (Tompa & Fuxreiter 2008)

2. Clamp model: Clamp model is possible when we are dealing with a protein that has two structured globular regions connected with one intrinsically disordered region (IDR) that serves as a linker segment in this case. Two structured regions bind following a regular lock-key mechanism of binding, while the function of the IDR is just to link them. Because IDR is very flexible, it enables different 3D positions of the two structured regions, meaning that they can easily adapt to the binding protein surface.

Figure 2: Representation of a clamp mechanism in forming a fuzzy complex: two structured regions (orange alpha helices) are connected with a disordered linker segment (in orange dotted line). (Miskei et al 2016)

3. Flanking model : This mechanism is similar to the clamp model. The difference is that in the clamp model the only disordered part is the linker segment, while the two domains that actually bind to the binding partner are structured (globular/ordered). In the case of a flanking model, the two domains that bind to the binding partner are disordered, as well as the linker segment. The two (or more) domains that bind to the binding partner are SLiMs (short linear motifs) that undergo folding upon binding. Because the linker segment is disordered, it retains its conformational freedom and therefore reduces the entropic penalty caused by binding of the SLiMs to the binding partner. Importantly, the flanking regions modulate the nature of the interaction between the SLiMs and the binding partner by contributing to the affinity and specificity of the interaction (and by “keeping the binding sites safe”).

Figure 3: Representation of a flanking model: three SLiMs can be seen in orange, forming the alpha helices upon binding to a partner protein shown in gray. The orange dotted line represents a flanking region which is also a linker segment between different SLiMs. (Miskei et al 2016)

Flanking model is very interesting because there is a folding upon binding and static disorder upon binding present in the same IDP chain. We can further describe the modes of binding within the flanking model as: avidity and allovalency. In short, avidity is a mechanism of binding which entails two or more binding sites present on an IDP (so two or more SLiMs connected with a linker segment), that are complementary to two or more binding sites present on its target protein (remember here that in the case of a polymorphic model the IDP conformations didn’t need to be complementary to the binding partner 3D structure). In order to achieve avidity, it’s important that the number of binding sites on the IDP is equal to the number of binding sites on a binding partner and at no point in time should the contacts interchange. An advantage of this binding mechanism is that once the initial contact is made, a cooperative effect takes place, facilitating further interactions. Moreover, the increased local concentration from the first binding event, combined with the lowered entropic penalty when binding only one IDP results in a greater binding affinity and the interaction is more thermodynamically favorable. 

Conversely, allovalency models a system where there are multiple binding sites in tandem on an IDP that are complementary to a single binding site on its partner. Once the first contact is made, the rest of the complementary binding sites of the IDP start competing for this spot so that even when the first contact dissociates (due to low binding affinity which we discussed earlier), anoher complementary binding spot od an IDP is close enough to form a contatc. In this way, some part of an IDP is always bound to the binding partner, but the binding spots on the IDP are exchaning relatively quickly. 

Figure 4: Representation of the allovalency binding mechanism in the flanking mode – on the left we can see the first contact being made between an IDP and its binding partner. This contact dissolves quickly, but due to the high concentration of complementary binding sites on the IDP, the binding site is quickly filled with another binding domain from the IDP (another SLiM), as shown on the right hand side. (Morris et al 2021)

Back to the binding modes, finally we have

4. Random model: The random fuzzy model describes a situation where both the IDP and its target protein have a number of interaction sites and the interaction between each site is not restricted by any specificity. The random model works similar to the allovalency: first one nonspecific low affinity binding takes place between one binding site on the IDP and one on the binding partner protein. Even though this interaction dissolves quickly, because of the large number of putative binding sites in a close proximity, the binding is resumed even quicker. Random fuzziness represents the extreme case of disorder where there is little to no secondary structure induced upon binding of the IDP to a binding partner and the IDP retains a high degree of conformational freedom, making this model hard to characterise (both experimentaly and computationaly).

Figure 5: Representation of a random model of fuzzy complex. Each colored “blob” in orange and blue represent a binding site of an IDP, while the target protein is shown in gray. (source: Wikipedia)

To be continued

Even though it might seem like we have it all figured out with the classes and groups we formed in an attempt to describe and understand the wild nature of IDPs when they interact with other proteins, the reality might not be so optimistic. We are still far from fully understanding these complex mechanisms, let alone predicting this kind of behaviour, but with the growing interest in this very special group of proteins, we will surely soon be in a much better position. I will do my best to cover a part of that amazing journey here so stay with us for more stories about the world of the mischievous IDPs 🙂


  • Miskei M, Antal C, Fuxreiter M. FuzDB: database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies. Nuc Acid Res. (2017) 4;45(D1):D228-D235.
  • Morris O, Torpey J, Isaacscon RL. Intrinsically disordered proteins: Modes of binding with emphasis on disordered domains. Open Biol. (2021) 11: 210222.
  • Tompa P, Fuxreiter M. Fuzzy complexes: polymorphism and structural disorder in protein–protein interactions. Trends Biochem Sci. (2008) 33:1 (2-8).
  • Wikipedia. Fuzzy complex. (date of usage: 1.5.2023.)