I have recently written, for WIREs Computational Molecular Science, a review article on the use of Principal Component Analysis (PCA) in the study of dynamical systems, with a particular focus on molecular dynamics (MD) simulations of biomolecules [1]. The aim of this work is to provide a clear and practical overview of how PCA has become a central tool for extracting meaningful collective motions from high-dimensional simulation data, and how modern methodological extensions continue to expand its capabilities.

Surfactants are everywhere in protein science — from biochemical laboratories to industrial detergents. Among them, sodium dodecyl sulfate (SDS) is perhaps the most famous (or infamous), widely used for its ability to bind, deactivate, and often denature proteins. Despite decades of experimental and theoretical work, the molecular details of how surfactants bind to protein surfaces are still not fully understood. In my recent study, “Binding Dynamics of Linear Alkyl-sulfates of Different Chain Lengths on a Protein Surface” [1], I have explored this problem using molecular dynamics (MD) simulations, focusing on how the length of the surfactant’s hydrocarbon chain influences protein binding.

In a recent molecular dynamics study [1] in collaboration with Prof. K. J. Karki (Department of Physics, Guangdong Technion-Israel Institute of Technology in China), we explored how EthylChlorophyllide a behaves when confined between two silica surfaces — a situation relevant for artificial photosynthesis, nanomaterials, and bio-inspired light-harvesting systems. Chlorophylls are among the most important molecules on Earth. They enable plants, algae, and photosynthetic bacteria to convert sunlight into chemical energy. Yet, outside their natural protein environment, chlorophyll molecules are fragile as they can easily lose their central magnesium ion (demetallation), they degrade under light, and they tend to aggregate uncontrollably in solution.

In natural photosynthetic systems, proteins protect and organize chlorophylls. Reproducing this level of control in artificial systems remains a major challenge. One promising strategy is nanoconfinement — trapping chlorophyll derivatives inside well-defined inorganic structures such as silica nanopores.

Proteins are not always rigid structures. Many of their most important parts — linkers, loops, and disordered regions — are highly flexible, constantly changing shape in solution. To understand how these regions behave, scientists often study short model peptides that capture the essential physics of flexibility. In a new article [1], I have explored the behavior of glycine- and serine-rich octapeptides using molecular dynamics simulations combined with concepts from FRET (Förster Resonance Energy Transfer) spectroscopy (see also my previous post).

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 , 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.