Computational

In docking we have to consider the intramolecular forces (covalent): bond stretching, angle bending and

bond rotation. While the intermolecular forces (non-covalent) are described with: the Van der Waals term

(dispersion and repulsion term) that model the shape complementarity of the ligand with the receptor, the

electrostatic term, the hydrogen bonding term (not considered in MM) and the Pi-Stacking (they can be

end-to-face or the more stable face-to-face), Cation-Pi and Metal Ions interactions terms . In sum, those

specific terms give the specificity of recognition. Other important term is the desolvation term: it is

important to take into account the role of water within the binding cavity (desolvation because when the

ligand enters the binding cavity pulls out the molecules of water of the bulk solvent). Energy models often

account for changes in torsional entropy with a term related to the number of rotatable bonds in the

ligand. All of those terms are added for each of the trial poses that are done during the search and they are

used to find the best conformation and to estimate what the free energy is. These terms are very similar to

the force fields regarding molecular dynamics simulations.

4. Which is the condition that MD simulation must fulfill in order to be considered as a "virtual

microscope"?

Only if we follow a strict protocol we can consider our MD simulations as a “ virtual microscope”. So we

have to include our molecule (e.g. protein) in a box of water (to reproduce the environment); to create the

periodic boundaries conditions (to simulate a more physiological condition); to set the cut off (to prevent

the proteins to see their specular image); to set up the minimization parameters (in order to explore all the

possible conformations with different energies and to choose the one with the lower energy); to set up the

equilibration parameters (because our protein's structure comes from XRC and cryo-EM, so it's crystallized):

thermostats (NVT) and barostats (NPT); to define a force field and then parametrizing (if it has not already

been done for the molecules of interest). If we want to do the simulation for a membrane protein we have

also to select the membrane composition and to orientate the protein in the membrane slab.

5. What are the time/space scales that can be explored with molecular dynamics simulations?Which

kind of biological/chemical events can be studied with these techniques? VEDI ↑

6. Discussion about solvent treatment in MD simulations and docking.

Biomolecules should be model considering the solvent effect. The solvent in MD simulation can be

considered in two ways: Explicitly Solvent: explicit molecules of solvent (usually water) are included in the

simulation, this means that more atoms must be simulated augmenting the computational time. Explicit

solvent models treat explicitly (i.e. the coordinates and usually at least some of the molecular degrees of

freedom are included) the solvent molecules. Implicitly Solvent: in order to reduce the computational time

and cost in MD simulations but at the same time keeping a certain level of accuracy, a new approach for

solvent modeling has been developed. Implicit solvents or continuum solvents, are models in which one

accepts the assumption that explicit solvent molecules can be replaced by a homogeneously polarizable

medium as long as this medium, to a good approximation, gives equivalent properties. The main parameter

is the dielectric constant (ε), this is often supplemented with further parameters, for example solvent

surface tension.

In docking, the solvent effect is treated adding the desolvation term that takes into account enthalpic

contributions and entropic contributions of the release of water molecules that surround the ligand and fill

the binding site (when the ligand binds there will be a bunch of water molecules that must be released in

order to not prevent the binding); it also models the hydrophobicity effect so that desolvation term is going

to be favourable for burial of carbon-rich areas of the ligand and carbon-rich areas of the receptor. A big

problem with water molecules that docking programs cannot solve for the moment, at least in an

automatic way is when the waters are forming actually the binding cavity: when the waters are helping or

are considered as bridges between the ligand and the protein.

1. Description of autodock suite for docking.

The Autodock suite is a state of the art package that is used in numerous laboratories and for different

application such as docking and virtual screening. Vina uses a simple scoring function and rapid gradient-

optimization conformational search, while Autodock relies on an empirical free-energy force field and

rapid Lamarckian genetic algorithm search method. Vina is a very fast method but uses several

approximations in the scoring function. Indeed, the latter does not include any electrostatic terms,

hydrogen atoms and furthermore it uses spherically symmetric hydrogen bond potentials to model

hydrogen bond interactions. Vina has shown to give good results when ligands have typical biological size

and composition.

On the other hand, the Autodock score functions comprises physically based terms. Indeed, the latter

includes explicit polar hydrogen, a term for the electrostatic contribution and a term for the directional

hydrogen-bonding interactions. The Autodock score function is already optimized to give successful results

for different biological systems.

2. Discussion about periodic boundary conditions.

The biological system under investigation is finite so boundaries must be considered. In

principle, there are two options that can be adopted. In the first choice, the system can

be inserted in a virtual box, thus it has a sort of “walls”. Consequently, this could lead

to surface effects (artifacts) in the MD simulation because of the behavior of atoms in

proximity of the walls. Thus, the solution to this problem, more frequently used, is the

periodic boundary conditions (PBC). On these terms, there are no rigid and

impenetrable boundaries. Atoms that get out in on one side, they get in on the

opposite side at the same velocity (like PAC-MAN). In practical terms, the box results to

be infinitely replicated in each direction. PBC can still produce minor artifacts. Indeed,

the user, during the MD setup, must carefully evaluate the volume of the virtual box

(cut off) to avoid interactions between the molecule of interest and its own specular

images (that is something of not physiological). That’s why for the long-range

interactions, which are the coulombic and van der Walls interactions, we will set a cut

off that is equivalent at least to the distance of the last atom on a protein till the wall of the box itself.

3. Protein folding (description in the ambit of computational biology)

Today being able to simulate the folding of a protein is impossible (too much computational effort), unless

there are some particular conditions: the protein should not be very big, no more that 200 aa and the

structure of the protein should be very compact, for example full of secondary structure elements (perhaps

with disulfide bridges, that stabilize the compact conformation of the protein).

4. Choice of simulation approaches: discussion about lenght and time scales

Molecular processes present time scale that span from the fs to the second and from pm to µm. In

principle, we should simulate each biological system by using the Schrödinger equation (QM). In this case,

the computation time and cost would be infinite so, we must choose the correct computation approach in

agreement with the time and lengths scale of the system that we want to investigate. It is certain that we

need to find a compromise between simulation speed and accuracy in the description of the system.

Broadly speaking we can present three levels of detail:

-2 -1

1. Quantum mechanics (QM): 10-1000 atoms for 10 -10 ns.

4 5

2. Atomistic molecular mechanics (MM): 10 -10 atoms for 10-1000 ns.

3 9 9

3. Coarse-grained molecular mechanics (CG): ~10 -10 atoms for ~1-10 ns.

5. Principle of homology modeling, advancements and limitations

Homology or comparative modeling methods are based on the idea that evolutionary related proteins share

a similar structure. The quality of the predicted models is thus correlated with the evolutionary distance

between the template (member of the family with known structure) and the target proteins. So, Homology

model techniques are the most reliable and accurate methods to generate structures. Although homology

modeling can boast a number of successes in many applications, it should be noted that the generated

models are predictions and could present some inaccuracies. It is well-known that homology modeling

methods depend strongly on both the sequence identity (SI) between the target and the templates and the

accuracy of alignment. SI < 50% generally leads to structural divergence between the models and the actual

experimental structure, measured as Cα atom RMSD, larger than 1 Å.

Actually, two proteins with SI > 35% were shown to share the same fold. Finally, with low target template SI

(between 10% and 25%), the comparative models might contain serious errors, thus, it is strongly

recommended the introduction of experimental information such as ligand information, site-directed

mutagenesis, and other experimental restraints to improve the accuracy of the model. Structural Deviations

of a target region that is aligned correctly with the template. The quality of the alignment is one the major

problems in homology modeling, especially when the SI falls below 20%. Misaligned regions correspond to

errors in positioning the target residues on the template fold; resulting in an unreliable model. Multiple

sequence alignment (MSA) and hidden Markov models (HMM) profiles approaches combined with manual

inspection and curation of the alignments are strongly recommended to investigate possible errors and

adjust key motifs in the alignment.

Choose one of the following

6. What is "Structure-based drug design" and which are the principal protein and small molecule

databases?

If the three-dimensional structure of a disease-related drug target is known, the most commonly used

CADD techniques are structure-based. In SBDD the therapeutics are designed based on the knowledge of

the target structure. Two commonly used methods in SBDD are molecular docking approaches and de novo

ligand (antagonists, agonists, inhibitors, etc. of a target) design. Molecular dynamics (MD) simulations are

frequently used in SBDD to give insights into not only how ligands bind with target proteins but also the

pathways of interaction and to account for target flexibility. This is especially important when drug targets

are membrane proteins where membrane permeability is considered to be important for drugs to be

useful.

The Protein Databank (PDB), which was first introduced in 1970s, is a global resource that contains a wealth

of 3D information about experimentally determined biological macromolecules. The structures in the PDB

are individual macromolecules, prot