Project idea

• At the universe initiation level, the context will pass as an argument (either as a name or a guesser object), in addition, a to_guess list will pass the desired attributes to be guessed.

• Also, the guesser class could be called after the universe's creation with the guess_topology_attr() API with the same spirit as the above implementation.

• If the user didn’t specify a context to the universe, a DefaultGuesser will be called, this guesser class will have the current generic guess methods.

• The universe then will check the validity of the passed arguments and raise the appropriate errors accordingly.

• Different guessing methods will through warnings/error messages depending on the results it got, the output messages should precisely describe the universe updates with a warning about failed processes.

Advantages of a local database:

1. a. Fast to lookup

2. b. Fewer dependencies on external packages

Disadvantages:

1. a. Need regular maintenance and design optimization

2. b. The database size could be huge

• Use an API to get data from the online chemical component dictionary

pdbeccdutils is an API built by PDB to retrieve data from the online CCD, it downloads and reads mmCIF data format, then store the residue data in a nested dictionary called component. By the appropriate key, we can get the data that we want.

Martini forcefield is concerned with dealing with coarse-grained particles rather than working with all atoms models. So, its parameters need special care regarding guessing their properties. In the Martini guesser implementation, we will focus on the newly released version, Martini 3. In the new version, seven bead types exist according to chemical properties, and they are further subdivided with numbers and labels according to polarity and hydrogen donor/acceptor properties. In addition, there are three beads’ sizes according to the number of non-hydrogen atoms forming it.

The bead's name carries lots of chemical properties that can be of benefit in various analytical processes. For our first version of Martini guesser, we will initially be concerned with bead types (atom type in MDAnalysis), masses, and charges guessing.

• Bead types

Martini has a well-established database for almost all the beads, from which most polymers (lipids, proteins, carbohydrates, etc.) are built. We can build our local database to store these data, and by proper mapping, we can guess the bead types for known polymers. However, if the residue is unknown by our database, we can check if the bead name is a valid one, then we can safely consider it the bead type.

• Masses

Martini has three bead sizes, each of which has a default mass regardless of the underlying type of atoms. The default masses of R, S, and T beads are 72, 54, and 36 amu, respectively, this information lies in bead type. However, if the molecule has a virtual site, its mass must be distributed over the other beads in the molecule, which make the virtual site an important attribute to store.

• Charges

Monovalent and divalent atoms are distinguished by the beads types Q and D. Regarding the sign of the charge, there are two clues to make the guess; the first one is the positive and negative labels (p/n), which are given to some charged molecules that possess hydrogen bonding capabilities (p for positive acceptor molecule and n for negative donor molecule), the second one is the Martini bead assignment table for charged molecules, we can use this as a dictionary database for our guess_charge() method. However, there are some anions and cations that have common names, so depending on this table alone is not sufficient, so we can use residue names or the neighboring beads as a hint to make better guessing.