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Water energy contributions to structural inhomogeneity of curcubit[8]uril

posted Jun 15, 2018, 4:54 PM by Fatlum Hajredini   [ updated Jun 17, 2018, 7:09 AM ]

Expulsion of water from cavities on host binding sites can contribute a great deal of energy to binding of ligands. This can be attributed to the gain in entropy as the waters become unstructured. Accounting for such effects in binding free energy calculations greatly improves the prediction accuracy. This effect is likely to also contribute to the structural reorganization of macromolecules. Using molecular dynamics simulation and the recently developed SSTMap suite we investigate the contribution of water thermodynamics to the tendency of curcubit[8]uril to assuming a compacted structure. Hydration site analysis shows that structuring of waters at the cavity of cb8 carries an entropic penalty which was expected to be relieved upon compaction of cb8. A cb8ligand complex had overall more favorable hydration upon assuming a more compacted structure compared to its more extended counterpart, suggesting that the compaction was due to water expulsion. To investigate whether the same phenomenon would be observed in the cb8 complex alone, simulations were carried in an explicit solvation model, and an implicit solvation model which lacks water structure information. Contrary to expectation, when simulated in the implicit solvation model cb8 is predominantly compacted, and also assumes an overall more compact structure compared to any of the conformations in the explicit water simulation. Simulations in explicit solvent show a wide distribution of states, with the extended conformation being much more populated then the counterpart in the implicit solvent simulation. Taken together, these findings suggest that structural networks of water, when treated explicitly can have a significant impact on the structural reorganization of macromolecules. 


                    State Distribution of cb8 when simulated in implicit solvent (blue), or explicit solvent (red) as determined by differences in their radii of gyration. 

Tripeptide Stabilized Nanoemulsions for Cancer Therapy

posted May 24, 2018, 9:18 PM by Marium

A carboplatin derivative consisting of two oleic acids attached to a platinum head, used to kill cancer cells, is investigated in this project through chemical simulation software. One of the challenges of this class of anticancerous drugs is its high level of toxicity to the human body, which results in low clinical dosings of the drug and ultimately ineffective therapy. Thus, for drug delivery to the human body, the carboplatin derivative requires an emulsifier to stabilize it and shield its toxic agents. The emulsifiers used in this project are tripeptides because of their biodegradability and it has been shown that they are promising self assembly candidates in a previous study. In the first phase of this project, general features of oleic acid aggregates in water solution are investigated. In the second, tripeptides, specifically KYF (Lysine- Tyrosine - Phenylalanine) and DFF (Aspartic Acid- Diphenylalanine), are added to these aggregates in order to study the tripeptide stabilized nanoemulsions. The simulations gave insight about the interactions present in the nanoemulsions. 
Structure of KYF and oleic acid aggregate
Structure of KYF(green) and oleic acid aggregate
(carbons are gray, carboxyl groups are in red, amine groups are in blue)


Use of Umbrella Sampling Methods to Estimate Probability of CYFIP1p Helical Conformation

posted May 20, 2018, 11:58 AM by Megan Wang

Previously, we made modifications to the wild-type CYFIP1p to stabilize its α-helical structure and increase its binding efficiency to EIF4E. In this study, we utilized umbrella sampling methods to accelerate the sampling of possible peptide conformations, such that the probability of these conformations could be readily calculated from an MD simulation that would otherwise have taken months to collect a sufficient number of relevant samples. Using two Python scripts, we were able to apply a bias force onto CYFIP1p and measure the proportion of instances that CYFIP1p maintained a helical conformation. We effectively estimated the probability of such an outcome to be 0.18 percent if the bias force were nonexistent.

Potential bias force applied to interacting atoms in molecular system.  

Research Report

Possible Computational Evidence for Enhanced α-Helix in Modified CYFIP1-derived Peptides

posted Dec 7, 2017, 11:03 AM by Megan Wang   [ updated Mar 17, 2018, 9:29 PM by Emilio Gallicchio ]


Dysregulation of the eukaryotic translation initiation factor (eIf4E) has been shown to exist in Fragile X Syndrome, a leading cause of intellectual disabilities such as autism. The cytoplasmic FMRP-interacting protein 1 (CYFIP1) plays a key regulatory role in repressing associated mRNA translation by binding to eIF4E. A crucial secondary structure element in the interactions between a CYFIP1-derived peptide (CYFIP1p) and eIF4E is the α-helix, and as a consequence, improving the persistence of α-helicity of the peptide could lead to improved binding efficiency. In our study, we made computer-aided chemical modifications in an effort to further stabilize the α-helix structures of peptides derived from wild-type CYFIP1p. Our findings suggest the addition of a staple comprised of alkyls or an aromatic ring has no significant impact on the secondary structure elements of the CYFIP1-derived peptides. However, modifications comprised of a long chemical staple combined with a mutation from serine to alanine resulted in improved α-helix stability and thus, exhibited a potential for enhanced binding efficiency. 

Snapshot of the trajectory for a CYFIP1-derived peptide exhibiting α-helix fold. 



Gaussian-based Volume and Surface Area algorithm for GPUs

posted Jan 8, 2017, 9:26 AM by Emilio Gallicchio   [ updated Jun 5, 2018, 11:07 AM ]


The model proposed by Grant & Pickup [J. Phys. Chem. 99, 3503-3510 (1995)] is a well-established method to estimate accurately volume and surface areas of molecules. The method is based on the inclusion-exclusion formula of statistical physics (also known as the Poincaré formula). It states that the volume of an object composed of multiple overlapping bodies is given by the sum of the volumes of the bodies, minus the sum of the overlap volumes between pairs of bodies, plus the sum of the overlap volumes between triplets of bodies, and so on:
Atomic volumes are represented by Gaussian densities and overlap volumes are computed using standard Gaussian overlap integrals. The model is fully analytic. For example, the solvent-accessible surface area of an atom is obtained as the derivative of the molecular volume with respect to the atomic radius of an atom.

The model leads to a tree-based algorithm in which overlap volumes are recursively evaluated. 
overlap tree
We have used this model extensively to implement the non-polar component and the self-adjusting pair-wise descreening method for the Generalized Born solvation electrostatic component of the of the AGBNP solvation model. Our CPU implementation is based on a dept-first traversal of the tree.

Lately we have been working on a GPU implementation of the Gaussian volumetric model. As one can imagine, tree-based recursive algorithms do not easily lend themselves to GPUs. However, we finally found an efficient solution based on a flat arrays representation of the tree and breadth-first traversal (see paper in JCC).
GPU tree traversal
The resulting GPU implementation is 50 to 100 times faster than our best CPU implementation. With the help with the development team at Stanford, the algorithm is now available as a plugin of the OpenMM molecular mechanics package.





Dopamine D3 receptor antagonists

posted Oct 31, 2016, 1:16 PM by Pierpaolo Cordone   [ updated Feb 1, 2018, 9:10 PM by Emilio Gallicchio ]

According to Newman et al., in all D3 receptor antagonists a salt bridge between the protonated amine in the primary pharmcophore and Asp 110 is observed. Previous antagonists candidates having a stepholidine ring as a primary pharmacophore have been synthetized in our lab. The protonated nitrogen of the pharmacophore makes the same interaction with Asp 110 observed in Newman et al.10 Some of the molecules used in this study like 216F (figure 4 left) have the same feature. However, molecules like 217F don't do the same interaction (figure 4 right). In order to find out the reason why those molecules interact differently 216F and 217F were superimposed (figure 5). From the superimposition it looks that there is electronic repulsion between a cyano group in para of the aromatic ring and the carbonyl of Val 189. This repulsion would cause the molecule to rearrange in another way in order to fit in the receptor. This rearrangement would prevent the protonated amine of 217F to make the salt bridge with Asp 110 in the primary binding site (OBS). This phenomenon occurs in all molecules with a substitution in para.

Figure 4. left, SG-216F makes the salt bridge with Asp 110. Righ, SG-217F does not make the salt bridge interaction with Asp 110. 

Figure 5. SG-216F in blue and SG-217F in pink. SG-217F cannot make the salt bridge interaction because of the clash between the cyano group and valine 189.

Prediction of Binding Energy affinities of Cucurbituril clip(host) with various guests as a part of SAMPL5 Challenge

posted Apr 5, 2016, 8:15 PM by Divya Kaur

By: Divya K.Matta
 Ph.D student in Chemistry

The binding energies of Cucurbituril clip with various guests have been calculated using Binding energy distribution Analysis Method(BEDAM). The method employs AGBNP2(Analytic Generalized Born Plus NonPolar2) as an implicit(continuum) solvation model. Molecular Dynamic(MD) simulations were carried out to predict the binding free energies of these host-guest systems. Cucurbituril clip is deprotonated and has a charge of -4 due to the presence of four sulfonate groups.The values of Binding energies of these host guests system depends on the number of factors such as the strength of interactions between them, for example, depending upon whether they have hydrogen bonding, electrostatic or hydrophobic interactions, the binding energies vary accordingly. Also, it depends on the charge of guests molecules whether they are neutral or ionised.With the guidance of Prof.Emilio, I learnt the BEDAM methodology and its applications on host-guest systems. 


Image :Interaction of Cucurbituril clip(host) with the guest 

Poster Presentation on the results of SAMPL5 Host-Guest Challenge at the D3R workshop, March 2016

posted Mar 20, 2016, 8:31 PM by Rajat Kumar Pal   [ updated May 12, 2016, 3:36 PM by Emilio Gallicchio ]

Our lab participated in the SAMPL5 host-guest blind challenge During September 2015 to February 2016. The challenge aimed at the use of computational methodologies to study and rank the binding of a set of guest molecules with three specific hosts. In this work, Hydration Site Analysis(Young et al., 2007; Lazaridis, T. 1998) was used to score the water sites within the binding pocket to favor the binding of the guests and the binding free energy was calculated using BEDAM(Gallicchio et al. 2010) with the incorporation of salt effects.
A summary of the final result was presented as a talk and also as a poster at the SAMPL5 workshop.
SAMPL5 poster

Molecular Docking Investigation of Inhibitors of the Heat Shock Protein 90

posted Dec 30, 2015, 4:41 AM by Emilio Gallicchio   [ updated May 12, 2016, 3:33 PM ]

by Godfrey Rollins
as part of the CHEM 5130 Independent Research course at the Department of Chemistry at Brooklyn College, Fall 2015.


HSP90 is a heat shock protein that plays an important role in our bodies by causing them to function properly. This role includes assisting in correct protein folding as well as stabilizing them when they are exposed to high temperatures. However, cancerous cells rely on the protein for growth and survival. In addition to that, these cells produce more of this protein than their healthy counterparts. Inhibition of this protein makes it easier for the cancer cells to be destroyed. Several HSP90 inhibitors were discovered and synthesized and are tested against this protein. As there are few crystal structures of the inhibitors present, more structures need to be predicted in order to have a better on how these inhibitors alter their structure and bind to HSP90. In this project, we use computational docking methods in order to predict what these structures of the complexes the inhibitors form when they bind to HSP90.


We first start off by obtaining a structure of an HSP90 inhibitor. We check it to see that the atoms are correct and that the formal charge of the inhibitor is 0. In this example, we use BIIB021.


Secondly, we prepare possible structures of the inhibitor using LigPrep. This results in various protonation states and possible tautomers. Here, we see a protonated N atom on the left aromatic ring on BIIB021.


Thirdly, we create a receptor grid of a known crystal structure we obtained. In this example we will dock BIIB021 onto a receptor grid obtained from the crystal structure with PDB ID code: 4B7P This code consists of another inhibitor, NMS-E973 bound to HSP90. The receptor grid showing the binding site is seen below.


Finally, we dock our inhibitor onto the receptor grid.


From what we see here, BIIB021 in its protonated state forms a hydrogen bond with Aspartic Acid and a halogen bond with Lysine. This is one of the possible structures of BIIB021 when it binds to HSP90. As several of these inhibitors have different structures, there are different possible complex structures that can be predicted.

2D Structures of the inhibitors used in this study are available in the PDF file below. FP IC50 values are expressed in µM.

A Quantitative Assessment of Amyloid-like Association by Radius of Gyration in Multimeric Systems

posted Jun 25, 2015, 9:03 AM by Sajeev Saluja   [ updated Jun 25, 2015, 10:32 AM ]

by Sajeev Saluja

It is difficult to do research in the field of computational biology without facing a problem that one cannot solve with the tools available. Many times, most of the work put into analysis and manipulation of theoretical data goes into creating the tools to efficiently do the task at hand. In this case, we were studying oligomeric/multimeric peptide systems, and how they aggregated into a single structure:




We needed some way of determining -- in a single structure -- whether the peptides had aggregated or not, to give us a sense of the way a specific sequence aggregates, the structure it forms, and the speed and intensity at which it does so. To our dismay, no such measurement was present in the literature for our specific needs and formats. Thus, we created the Radius of Gyration Trajectory Tool. Please see the report attached for further details.

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