deletions | additions       diff --git a/untitled.tex b/untitled.tex  index b0af58f..e14b944 100644  --- a/untitled.tex  +++ b/untitled.tex 
...
FXIa  The best SMARTS expression and approach for FXIa performed significantly better than random performance for the FOMS, but not the VAMS method (cite table). Inhibition of FXIa relies on hydrogen-bonding with many residues. Only one of the pockets and its interaction with a His57 by hydrogen bonding and a stacking interaction are covered by our SMARTS expression and fragment pre-alignment (cite FXIa). There are many other hydrogen-bond interactions that include hydrogen bonding with Asp189, Gly216, and Gly218 backbones that are not covered by our SMARTS expression and fragment pre-alignment. Similar to most other protein targets in the MUV dataset, FOMS would not fully capture the complete binding mode of compounds screened, which most likely led to its poor performance (cite table).   Docking of 2FDA with active compounds from the MUV dataset revealed 5 hydrophobic pockets, with only one being covered by our fragment pre-alignment. Many hydrogen-bonding interactions were revealed, including His57 and the inhibitor. Because our best SMARTS expression and fragment pre-alignment only covered the interaction with His57 and 1 of 5 potential pockets to be filled, it makes sense that FOMS did not yield high performance. More specific SMARTS expressions led to higher performance for VAMS, whereas the least specific SMARTS expression led to highest performance for FOMS (cite table). (explanation for both?)  HIV-RT  The best SMARTS expression and approach for HIV-RT did not perform significantly better than random performance for both methods (cite table). Inhibition of HIV-RT relies on many key interactions, including a stacking interaction with Tyr188, hydrogen-bonding with Lys103, and many important hydrophobic interactions with Val and Phe residues (cite HIV). Single and double mutation varieites of HIV-RT were tested against known inhibitors, with the conserved Tyr residue mutation causing a 600 fold increase in IC50. Our original fragment pre-alignment and SMARTS expression only covered the conserved Lys interaction, however we then performed pre-alignment to the Tyr residue interaction and observed better performance (cite Table). As with most of the other targets in the MUV dataset, there are multiple interactions necessary for proper binding of an HIV-RT inhibitor. FOMS will only cover a small part of the necessary interaction. Docking with 3DRP in smina for the active compounds from the MUV dataset were performed. This revealed 4 potential pockets to fill, with only one being covered by our fragment pre-alignment. In addition, hydrogen-bonding with Lys103 and potential stacking with Tyr188 were observed frequently. This indicates the importance of both interactions for proper HIV-RT binding. Since only one of the 4 pockets are covered by our fragment pre-alignment, it is possible that this is a contributing factor to the poor performance of FOMS. Less specific SMARTS expressions led to higher performance for FOMS, whereas more specific SMARTS expressions led to higher performance for VAMS (cite table). (explanation for both?) HSP 90  The best SMARTS expression and approach for Hsp90 performed significantly better than random performance for FOMS, but not for VAMS (cite table). Hsp 90 inhibitors interact with the ATP binding site. The most important interactions are with Asp93, Gly97, Lys58 , and Asn51 residues, with the study reporting the crystal structure highlighting Asp93 as the most important (cite HSP). Only the Asp93 interaction is covered by our SMARTS expressions and fragment pre-alignments, while the other interactions are also key for Hsp90 inhibition, with the Gly backbone being second in importance. As with other targets in the MUV dataset, the poor performance of FOMS relative to random performance can be partially attributed to the large number of interactions that are not covered by our fragment selection due to the nature of the ligand binding mode. Docking with 2VCI in smina for the active compounds from the MUV dataset were performed. This revealed 4 potential hydrophobic pockets, with only one covered by our fragment pre-alignment. There were also many active conformers hydrogen-bonding with Asp93 and at least one other residue as well. These docking results indicate the importance of inclusion of every hydrogen-bonding interaction and more than one hydrophobic pocket for Hsp90 inhibition. Our SMARTS expression and fragment pre-alignment only covered one of these interactions and one of the pockets, which most likely led to FOMS not having high performance. More specific SMARTS expressions led to higher performance for FOMS and VAMS (cite table). This makes sense as the more specific fragment choice would help both methods select compounds that can make the hydrogen-bond with Asp93.  PKA  The best SMARTS expression and approach for PKA performed significantly better than random performance for both FOMS and VAMS (cite stats table). The FOMS approach was significantly better than the VAMS approach for PKA (cite stats table). Like many other targets in this dataset, PKA inhibitors interact with the ATP binding site. These inhibitors are relatively smaller in size compared to other inhibitors and have few electrostatic interactions. In the example of 1Q8U and its inhibitor, there is only one hydrogen bond with Val123, where the rest of the ligand makes multiple Van der Waals contacts with other parts of the target (cite PKA). SMARTS expressions and fragment pre-alignment covers the only electrostatic interaction and most of the hydrophobic interactions involved in PKA inhibitors, which most likely led to the high performance of FOMS (cite table).  More specific SMARTS expressions led to higher performance for FOMS (cite table). This makes sense as the more specific fragment choice would cover the only necessary hydrogen-bond interaction with the receptor. Less specific SMARTS expressions led to higher performance for the VAMS method (cite table). (explanation for why this is).  Rho-Kinase2  The best SMARTS expression and approach for Rho-Kinase2 performed significantly better than random performance for both FOMS and VAMS (cite stats table). The FOMS approach was significantly better than the VAMS approach for Rho-Kinase2 (cite stats table). Similar to PKA, Rho-Kinase2 inhibitors are relatively small compared to other inhibtors and have few electrostatic interactions. In the case of 2H9V and its inhibitor, there are three hydrogen-bonding interactions with conserved Met, Asn, and Asp. There are also important hydrophobic interactions with other residues inside the pocket (cite Rho). Our SMARTS expression and fragment pre-alignment covers the hydrogen-bonding interaction with the Met and important hydrophobic interactions with conserved Val and Leu residues. This most likely led to the high performance of FOMS for Rho-Kinase2 (cite table).  More specific SMARTS expressions led to lower performance for both the FOMS and VAMS methods. (explanation for why this is ).