The Mighty Roto-Microscope

I am happy to announce our second Instructable project. Like the first one, it was a long-standing idea that was rolling in my mind for a long time. The current limited travelling mobility due to the COVID offered more time to develop this idea during my vacation. In a joyful collaboration with my son Leonardo, we managed to realize this useful device in a very short time.

This project aimed to develop a device that integrated with a cheap USB microscope allows taking 3D pictures of small samples. The project is meant to be an education STEM activity to learn using Arduino, 3D image reconstruction, and 3D printing by creating a useful piece of equipment for doing some exciting science activity. Like my previous project, it is also a moment to share good and educative time with my family and in particular, my elder son Leonardo that helped me in creating this instructable and evaluating the device as an enthusiastic STEM student. This time, also my lovely wife helps me to make a video of the assembly of the equipment!

The roto-microscope allows controlling the position of a simple USB microscope around the sample. This allows to take accurate pictures from different angles and not just from the top as in the traditional microscopes. This is not a new idea as there are professional microscopes. However, this device means to be affordable for a student and still provides some similar results and a lot of fun in building it. Other similar and excellent OpenSource projects are available (see, for example, the Ladybug microscope, the Lego microscope, and the OpenScan project), our project adds an additional option and I hope that you enjoy making it as we did!

If you find it an interesting device then instructions on how to build it are on our Instructable.

The Magic Sand Slicer

We have published for the first time a project on Instructables: a website specialised in publishing interesting DIY projects by an effervescent community of makers and educators.

The project is called the Magic-Sand Slicer and it is an education project initially conceived as a STEM activity to learn using Arduino, a 3D printer, and some exciting science. It is also a collaboration with my son Leonardo who helped me in evaluating the device as a STEM student. We have learned a lot together, and we want to share the results of this long journey. This project aims to create a device that automatically makes sections of a cylinder of easy-to-cut coloured material. That can be used for practising 3D image reconstruction of the coloured blogs hidden in the column. The so-called Magic-Sand (c), also known with other trademarks names, becomes suitable for this experiment.
What is the point of making pictures of thin layers of sand and then reconstructing it digitally? Is it just for the fun of it? It varies on who is using it. However, students and teachers from different disciplines (e.g. geology, biology, medical) can find it a helpful education device to practice with image reconstruction from the serial sections. It could also be of interest to a geologist interested in sedimentary material plasticity to study rock and the secrets it beholds, or to a process, engineering to emulate the packing of fine granular materials. Finally, an artist can make a fantastic program of unravelling magic forms generated by packing coloured sand. 

I was surprised that the project got so much interest in a very short time and I thank the Instructable community for their nice welcome! If you like to know more about the project (and try it!) then you can read our instructable here.

I also just realized that the Instructable was reviewed on the Arduino blog site by the Arduino team!

Seminar Series: Molecular Dynamics Simulation of Biomolecules

In this new series, I will post slides of seminars or lessons that I have delivered in the past years. Some of the reported information is updated, but still helpful. In some cases, I have added descriptions of the slide contents or references to other articles or the original paper where I describe my research results.
I hope you like the presentation, and remember to add your feedback and subscribe to have email notifications about my new blog posts.

In 1648, Isaac Newton published his first edition of the Principia Mathematica, one of the greatest scientific masterpieces of all time. On page 12 of this magnum opus, the famous three laws that bear his name and from which classical mathematical physics evolved are enunciated. More than 350 years after that publication, the same laws formulated to explain the motion of stars and planets remain valuable for us when trying to simplify the description of the atomic world. In the first decades of the last century, the birth of quantum mechanics marked the beginning of the detailed description of atomic physics. The equation of Schrödinger, to the same extent as Newton’s equations, allowed for the mathematically elegant formulation of the shining theoretical intuitions and the experimental data accumulated in the previous decades. Although this equation could be used in principle to describe any molecular system’s physicochemical behaviour, it is impossible to resolve analytically when the number of electrons is more than two. The invention of electronic computers after World War II facilitated the numerical solution of this equation for polyatomic systems. However, despite the continuous and rapid development of computer performance, the ab-initio quantum-mechanical approach to describe static and dynamic properties of molecules containing hundreds or even thousands of atoms, as for biological macromolecules, is still far from becoming a standard computational tool. This approach requires many calculations that can be proportional to N^{3-5}, where N is the total number of electrons in the system. It was clear that a reduction, using ad hoc approximations, of the description of the dynamic behaviour of atoms using a classic physics model would be necessary to overcome this problem. In the classical representation, the electrons on the atoms are not explicitly considered, but their mean-field effect is taken into account. Alder and Wainwright performed the first simulation of an atomic fluid using this approximation approximately 63 years ago (1957). They developed and used the method to study simple fluids by means of a model representing atoms as discs and rigid spheres. These first pioneer studies mark the birth of the classical molecular dynamics (MD) simulation technique. The successive use of more realistic interaction potentials has allowed obtaining simulations comparable to experimental data, showing that MD can be a valuable tool for surveying the microscopical properties of physical systems. The first simulations of this type were carried out by Rahman and Verlet (1964): in these simulations, a Lennard-Jones-type potential was used to describe the atomic interactions of argon in the liquid state. Another significant hallmark in this field was the simulation of the first protein (the bovine pancreatic trypsin inhibitor) by McCammon and Karplus in 1977. In the following years, the success obtained in reproducing structural properties of proteins and other macromolecules led to a great spread of the MD within structural biology studies. The continuous increase of computer power and improvement of programming languages has concurred with further refinement of the technique. Its application was progressively expanded to more complex biological systems comprising large protein complexes in a membrane environment. In this way, MD is becoming a powerful and flexible tool with applications in disparate fields, from structural biology to material science.

Continue reading

1971-2021: The Molecular Dynamics of Liquid Water turns Fifty

This year signs a landmark in the history of the molecular dynamics (MD) simulation method. Half a century ago 1971, Aneesur Rhaman and Frank H. Stillinger [1] published a pioneering work on the MD simulation of liquid water in the Journal of Chemical Physics. The journal received the article on May the 6th, 1971, and accepted it in October. Just seven years before (1964), the physicist A. Rahman (24 August 1927 – 6 June 1987) pioneered the computational method of MD when he published the first MD simulation study of liquid Argon [2]. The 1971 article is also the first MD study of a molecule in a condensed phase. It signed an important milestones water models are the most studied and used molecular models in MD simulations.

However, despite its structural simplicity, after 50 years of intense study, a perfect model for MD simulation is not yet available. By a perfect model, I mean one capable of computationally conveniently reproducing all the properties of water molecules in the range of possible applications. A water model needs to balance its computational simplicity required to keep computational costs for the simulations of large macromolecules in solution limited, and the accuracy to reproduce the physical chemistry properties accurately. For the most used but still 40-year-old models, this compromise was the equivalent of the inconveniently short blanket: pulling on one side would fit some properties but leave others loose. However, the continuous availability of inexpensive computer power and the advancements in simulation algorithms have allowed for significant progress in developing more accurate and efficient water models. In fact, several excellent models have been proposed since the R&S publication that can reproduce very well properties at 298 K or in a limited range of temperatures. In an updated but comprehensive article written 30 years later in the R&S paper, Guillot reported more than 100 proposed classic models for MD or Monte Carlo simulations published up to then [3]. Nineteen years after the publication of Guillot’s review, new and more sophisticated models have been proposed; nevertheless, the most used ones continue to bring the legacy of the R&S model.

In the late 1970s and early 1980s, the first molecular dynamics simulations of water were conducted using simplified models. These models represented water molecules as spheres interacting via pairwise Lennard-Jones potentials, capturing the intermolecular forces between the molecules. These developments in modeling water from molecular dynamics simulations have contributed significantly to our understanding of water’s structure, thermodynamics, and dynamics at the molecular level. They have provided insights into a wide range of phenomena, including solvation, hydrophobic interactions, phase transitions, and biomolecular interactions involving water molecules. In the following list, there is a summary of commonly used models for simulating water in molecular dynamics simulations.

  1. The SPC (Simple Point Charge) and SPC/E (Extended) developed by Prof. HJC Berendsen and collaborators a the University of Groningen (The Netherlands) [4] are widely used water models in molecular dynamics simulations. These models consider three atoms per water molecule but have different parameters for the Lennard-Jones potential and partial charges. The SPC/E model, in particular, has successfully reproduced various water properties, including the density and structure of liquid water.
    • Lennard-Jones Potential: The SPC models also utilize the Lennard-Jones potential to describe the van der Waals interactions between water molecules. The parameters are adjusted to reproduce various properties of water, including the radial distribution function.
    • Charges: The SPC models assign partial charges to the oxygen and hydrogen atoms in a manner that reproduces the dipole moment and electric field properties of water. These charges are carefully chosen to ensure an accurate representation of the water molecule.
  2. The TIP3P (Transferable Intermolecular Potential 3 Points) model developed by Prof. W. Jorgensen and collaborators at Yale University (USA) [4] is another widely-used water model. As the SPC model, it represents water molecules as three atoms: two hydrogen atoms and one oxygen atom. The oxygen atom has a partial negative charge, while each hydrogen atom has a partial positive charge. The model also includes harmonic bond stretching and angle bending terms to describe the covalent bonds and bond angles within water molecules.
    • Bond Stretching: The TIP3P model assumes harmonic bond stretching between oxygen and hydrogen atoms. The equilibrium bond length is typically set to 0.9572 Å, and the force constant represents the stiffness of the bond.
    • Angle Bending: The TIP3P model employs harmonic angle bending terms to describe the HOH angle. The equilibrium angle is typically set to 104.52 degrees, corresponding to the tetrahedral geometry of water.
    • Lennard-Jones Potential: The model uses the Lennard-Jones potential to describe the van der Waals interactions between water molecules. The parameters for the potential are chosen to reproduce the experimentally observed properties of water, such as the density and vaporization enthalpy.
    • Charges: The oxygen atom in the TIP3P model carries a partial negative charge, while each hydrogen atom has a partial positive charge. The charges are usually chosen to reproduce the dipole moment and electric field properties of water.
  3. The TIP4P model extends the TIP3P model by explicitly including a virtual site, referred to as the “dummy” or “massless” site, in addition to the three atoms. This dummy site is located along the HOH angle bisector and represents the lone pair of electrons on the oxygen atom. The TIP4P model improves the description of water properties, particularly the hydrogen bonding behavior.
    • Dummy Sites: The TIP4P model introduces a virtual site, referred to as the dummy or massless site, to represent the lone pair of electrons on the oxygen atom. This allows for a more accurate representation of the hydrogen bonding behavior in water.
    • Electrostatics: The TIP4P model includes a positive charge located on the dummy site, which balances the negative charge on the oxygen atom. This ensures that the model reproduces the correct dipole moment of water.
  4. Flexible Models: While rigid water models, such as TIP3P and SPC/E, assume fixed bond lengths and bond angles, flexible models introduce additional degrees of freedom by allowing parameter variations. Flexible water models, such as TIP5P and TIP4P/2005, consider the anharmonicity of bond stretching and angle bending, providing a more accurate description of water’s vibrational behavior.
    • Anharmonicity: Flexible water models, such as TIP4P/2005 and TIP5P, introduce anharmonic terms to account for the deviations from the harmonic behavior of bond stretching and angle bending. This allows for a more accurate representation of water’s vibrational properties.
    • Improved Parameters: Flexible models often incorporate refined parameters to better reproduce experimental data, such as vibrational spectra and thermodynamic properties, beyond what can be achieved with rigid models.
  5. Polarizable Models: Classical force fields typically assume fixed atomic charges on water molecules. However, water is a polar molecule whose charge distribution changes in response to its local environment. Polarizable models have been developed to capture the dynamic nature of water’s charge distribution. Polarizable force fields, such as the AMOEBA (Atomic Multipole Optimized Energetics for Biomolecular Simulation) force field, allow the partial charges on water molecules to vary with the atomic positions and local electric fields.
    • Drude Oscillators: Polarizable models, such as the AMOEBA force field, employ Drude oscillators to represent the fluctuating charge distributions in water. A Drude oscillator consists of a heavy atom (representing the oxygen atom) and a lighter particle (representing the hydrogen atom) connected by a harmonic bond.
    • Atomic Multipole Moments: Polarizable models use atomic multipole moments, including dipoles, quadrupoles, and higher-order moments, to describe the charge distribution of water molecules. The moments are allowed to vary with the positions of the atoms, capturing the dynamic nature of water’s charge distribution.

These model are the most communely used water models used in molecular dynamics simulations an din particular in simulation of biomolecular systems. The choice of model depends on the research question and the desired level of accuracy required for the simulation.

REFERENCES

  1. Rahman, A. and Stillinger, F.H., 1971. Molecular dynamics study of liquid water. The Journal of Chemical Physics55(7), pp.3336-3359.
  2. A. Rahman (1964). “Correlations in the Motion of Atoms in Liquid Argon”. Physical Review136: A405-A411.
  3. Guillot, B., 2002. A reappraisal of what we have learnt during three decades of computer simulations on water. Journal of molecular liquids101(1-3), pp.219-260.
  4. H. J. C. Berendsen, J. R. Grigera and T. P. Straatsma, The missing term in effective pair potentials, Journal of Physical Chemistry 91 (1987) 6269-6271.
  5. Jorgensen, W.L., Chandrasekhar, J., Madura, J.D., Impey, R.W. and Klein, M.L., 1983. Comparison of simple potential functions for simulating liquid water. The Journal of chemical physics79(2), pp.926-935.

A Practical Introduction to the C Language for Computational Chemistry. Part 3

Sphere. From Space, from Space, Sir: whence else?

Square. Pardon me, my Lord, but is not your Lordship already in Space, your Lordship and his humble servant, even at this moment?

Sphere. Pooh! what do you know of Space? Define Space.

Square. Space, my Lord, is height and breadth indefinitely prolonged.

Sphere. Exactly: you see you do not even know what Space is. You think it is of Two Dimensions only; but I have come to announce to you a Third — height, breadth, and length.

Square. Your Lordship is pleased to be merry. We also speak of length and height, or breadth and thickness, thus denoting Two Dimensions by four names.

Sphere. But I mean not only three names, but Three Dimensions.

Adapted from: 
Flatland: A romance of many dimensions by Edwin A. Abbott

ADVENTURE IN SPACELAND

In part 2 of this tutorial, we have learned how to use arrays and how to read atomic coordinates from a file. In the appendix, you can find an example of the solution to the exercises given in the previous tutorial.

In this third part, we are going to learn how to generate three-dimensional coordination of atoms in a cubic crystal lattice and how to calculate non-bonded molecular potential and the force acting among them.

Continue reading

Molekulare Maschinen: Die Coronavirus SARS-CoV-2 Bedrohung, Teil I.

Was Freunde mit und für uns tun, ist auch ein Erlebtes; denn es stärkt und fördert unsere Persönlichkeit. Was Feinde gegen uns unternehmen, erleben wir nicht, wir erfahren’s nur, lehnen’s ab und schützen uns dagegen wie gegen Frost, Sturm, Regen und Schloßenwetter oder sonst äußere Übel, die zu erwarten sind.

Johann Wolfgang von Goethe (1749-1832), Maximen und Reflexionen. Aphorismen und Aufzeichnungen.

Ein Virus ist Leben in der einfachsten Form. Es ist die minimalistische Reduktion eines Organismus auf seine wesentlichen Funktionselemente. Noch pragmatischer ist ein Virus ein Behälter mit genetischem Code mit einem effizienten molekularen Mechanismus, der es ihm ermöglicht, in eine Wirtszelle eines Organismus einzudringen, der sich selbstständig reproduzieren kann. Als molekulare Maschine kann ein Virus der Form und der zerstörerischen Kraft des Todessterns in der Star-Wars-Saga ähneln. Daher ist es eine Art molekulare Maschine, die wir absolut nicht in uns haben wollen!

Wie der große Goethe sagt, ist der Feind Teil unserer Erfahrung und wir müssen ihn jagen und uns tatsächlich vor anderen möglichen Feinden schützen. Dieser epische Naturkrieg veranlasste mich, diesen Blog zu starten, in dem ich mitteilen werde, was ich über diese gefährliche molekulare Maschine lerne.

Continue reading

Le Macchine Molecolari: La minaccia del Coronavirus SARS-CoV-2. Parte I

Difficilmente è vinto colui che sa conoscere le forze sue e quelle del nemico.

Nicollò Machiavelli in Dell’arte della guerra (1519-1520)

Un virus è la vita nella forma più semplice. È la riduzione minimalista di un organismo ai suoi elementi essenziali di funzionalità. Più pragmaticamente, un virus è un contenitore di codice genetico dotato di un efficiente meccanismo molecolare che gli consente d’invadere una cellula ospite di un organismo capace di riprodursi autonomamente. Come macchina molecolare, un virus può assomigliare nella forma e potere distruttivo, alla Morte Nera della saga di Star Wars. Pertanto, è un tipo di macchina molecolare che non vogliamo assolutamente avere dentro di noi!

La diffusione del coronavirus SARS-CoV-2 (COVID-19) ha prodotto una nuova pandemia, ovvero una infezione causata da un agente patogeno che colpisce l’intera popolazione di una specie vivente, in questo caso quella umana. Questa situazione di emergenza globale è il risultato di una competizione naturale tra specie viventi che ci rammenta di essere ancora un tassello nell’ecosistema di Gaia. Tuttavia, anche se sia sempre arduo da credere visto lo stato in cui abbiamo ridotto il nostro pianeta, siamo la forma di vita più intelligente nell’universo conosciuto. Quindi sarebbe abbastanza imbarazzante essere sconfitti da un nemico invisibile.

Continue reading

Nanoparticles in Biology and Medicine

I am very pleased to announce that the second edition of the book Nanoparticles in Biology and Medicine edited by Enrico Ferrari, Mikhail Soloviev is now out.

This fully updated volume presents a wide range of methods for synthesis, surface modification, characterization and application of nano-sized materials (nanoparticles) in the life science and medical fields, with a focus on drug delivery and diagnostics. Beginning with a section on the synthesis of nanoparticles and their applications, the book continues with detailed chapters on nanoparticle derivatization, bio-interface, and nanotoxicity, as well as nanoparticle characterization and advanced methods development. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Nanoparticles in Biology and Medicine: Methods and Protocols, Second Edition serves as an ideal guide for scientists at all levels of expertise to a wide range of biomedical and pharmaceutical applications including functional protein studies, drug delivery, immunochemistry, imaging, and more.

I have contributed with a chapter (14) titled The Molecular Dynamics Simulation of Peptides on Gold Nanosurfaces.

In this chapter a short tutorial on the preparation of molecular dynamics (MD) simulations for a peptide in solution at the interface of an uncoated gold nanosurface is given. Specifically, the step-by-step procedure will give guidance to set up the simulation of a 16 amino acid long antimicrobial peptide on a gold layer using the program Gromacs for Molecular Dynamics simulations.

Molecular Machines: the Coronavirus SARS-CoV-2 Menace. Part I

If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”

SunTzu. The Art of War

A virus is the Bauhaus of the form of life: the minimalist reduction of an organism to its essential element of functionality. More pragmatically, it is a container of genetic code provided with a smart mechanism that allows it to invade cells of another host organism. As a molecular machine, a virus can resemble in shape and destructive power the Death Star spaceship of the Star War saga. Therefore, it is a molecular machine that we do not definitively want to have within us!

The spread of the coronavirus SARS-CoV-2 has produced a new pandemic, i.e. an infection caused by a pathogen that affects the entire population of a living species, in this case the human one. This global emergency situation is the result of a natural competition between living species that reminds us that we are still a small brick of the Gaia ecosystem. However, although it is always difficult to believe given the state in which we have reduced our planet, we are the most intelligent life form in the known universe. So it would be quite embarrassing to be defeated by an invisible enemy.

Continue reading

Examples of the Particle in a Box Model Applications

In a previous article, I have shown a simple derivation of the properties of a quantum particle confined in a one-dimensional box. Although the model is straightforward with unrealistic assumptions, such as the infinite walls, it produced qualitative results that paved the way for the development of quantum chemistry. There are several example practical applications in chemistry and nanoscience where the particle in a box model can be applied or serves as a conceptual foundation.

Continue reading