Molecular mechanics and dynamics characterization of an in silico mutated protein: A stand‐alone lab module or support activity for in vivo and in vitro analyses of targeted proteins

Nascimento

Biochem Molecular Bio Educ

2021

A computational biochemistry laboratory, fitted for bioinformatics students, is presented. The molecular dynamics package GROMACS is used to prepare and simulate a solvated protein. Students analyze the trajectory with different available tools (GROMACS and VMD) to probe the structural stability of the protein during the simulation. Students are also required to make use of Python libraries and write their own code to probe non-covalent interactions between the amino acid side chains. Based on these results, students characterize the system in a qualitatively approach but also assess the importance of each specific interaction through time. This work mobilizes biochemical concepts and programming skills, fostering critical thinking and group work and developing presenting skills.

Section: Discussionmentioning

confidence: 99%

Molecular dynamics simulations and analysis for bioinformatics undergraduate students

Nascimento

Biochem Molecular Bio Educ

2021

Biochem Molecular Bio Educ

“…The graphs they produce are similar to those often included in scientific papers and textbooks that describe protein conformations. Although the second laboratory was designed to follow the first to provide familiarity with MD modeling, it could be taught as a standalone activity .…”

Section: Discussionmentioning

confidence: 99%

An undergraduate laboratory activity on molecular dynamics simulations

Spitznagel

Pritchett²,

Messina

et al. 2016

Self Cite

Vision and Change [AAAS, 2011] outlines a blueprint for modernizing biology education by addressing conceptual understanding of key concepts, such as the relationship between structure and function. The document also highlights skills necessary for student success in 21st century Biology, such as the use of modeling and simulation. Here we describe a laboratory activity that allows students to investigate the dynamic nature of protein structure and function through the use of a modeling technique known as molecular dynamics (MD). The activity takes place over two lab periods that are 3 hr each. The first lab period unpacks the basic approach behind MD simulations, beginning with the kinematic equations that all bioscience students learn in an introductory physics course. During this period students are taught rudimentary programming skills in Python while guided through simple modeling exercises that lead up to the simulation of the motion of a single atom. In the second lab period students extend concepts learned in the first period to develop skills in the use of expert MD software. Here students simulate and analyze changes in protein conformation resulting from temperature change, solvation, and phosphorylation. The article will describe how these activities can be carried out using free software packages, including Abalone and VMD/NAMD.

“…Various attempts to understand and describe the internal mechanics of molecular systems have already been reported . We did not, however, find any general‐purpose approach for studying a broader spectrum of cases, and in particular a methodology explaining the cause of a transition in terms of free energy contributions arising from certain parts of a molecule.…”

Section: Introductionmentioning

confidence: 94%

Toward the identification of molecular cogs

Dziubiński

Lesyng

2015

J. Comput. Chem.

Computer simulations of molecular systems allow determination of microscopic interactions between individual atoms or groups of atoms, as well as studies of intramolecular motions. Nevertheless, description of structural transformations at the mezoscopic level and identification of causal relations associated with these transformations is very difficult. Structural and functional properties are related to free energy changes. Therefore, to better understand structural and functional properties of molecular systems, it is required to deepen our knowledge of free energy contributions arising from molecular subsystems in the course of structural transformations. The method presented in this work quantifies the energetic contribution of each pair of atoms to the total free energy change along a given collective variable. Next, with the help of a genetic clustering algorithm, the method proposes a division of the system into two groups of atoms referred to as molecular cogs. Atoms which cooperate to push the system forward along a collective variable are referred to as forward cogs, and those which work in the opposite direction as reverse cogs. The procedure was tested on several small molecules for which the genetic clustering algorithm successfully found optimal partitionings into molecular cogs. The primary result of the method is a plot depicting the energetic contributions of the identified molecular cogs to the total Potential of Mean Force (PMF) change. Case-studies presented in this work should help better understand the implications of our approach, and were intended to pave the way to a future, publicly available implementation.