The Cheminformatics Network Blog

Cheminformatics, Bioinformatics, Systems Biology, Network Theory, Drug Design, Computational Chemistry and Computational Biology

Sunday, August 08, 2021

Networking Protein-Ligand Binding Sites

Sagar BhayyeSagar Bhayye, N. Sukumar
International Conference on Drug Discovery (ICDD) 2020
BITS Hyderabad, February 2020

Proteins are complex macromolecules that play a critical role in various body functions. Various functional proteins require small molecules (also known as ligands) to initiate various bio-chemical processes. The affinity of a ligand towards the specific protein is attributed to physicochemical properties of the protein-ligand binding site such as size, shape, surface charge distribution, etc. Because of this, a single ligand can bind to two or more structurally and functionally different proteins, leading to decrease in potency of drug molecules due to increase in distribution, and adverse drug reactions due to non-specific ligand binding to various proteins. In this study, a set of 4105 protein crystal structures belonging to different classes and species were used to construct a similarity network based on protein-ligand binding site properties. The Property-Encoded Shape Distributions (PESD) method was utilized to calculate protein-ligand binding site signatures and hence pair-wise similarities between ligand binding sites of the selected set of proteins. Metrics such as Euclidean, Chi-Square and Manhattan distances were used to calculate similarities between ligand binding sites of proteins, leading to quantitative understanding of their similarity relationships. Adjacency matrices calculated using these three different metrics were then used for construction of similarity networks. The networks were analyzed for properties such as vertex degree, average path length, degree distribution, average clustering coefficient, degree assortativity, modularity and different centrality measures. Properties of the three similarity networks were compared with the Erdӧs-Renyi random network. The Euclidean network shows higher average clustering coefficient and lower average path length than the Erdӧs-Renyi random network, indicating small world behavior.

 


Wednesday, July 11, 2018

What does Electron Density Analysis tell us about Bonding in Transition Metal-doped Boron and Carbon Clusters?

Sagamore XIX Conference on Quantum Crystallography
Halifax, Canada, July 11, 2018

Video link: https://youtu.be/qxu5uALd6Xs


N. Sukumar1, Pinaki Saha2, Amol B. Rahane3, Rudra Agarwal4, Vijay Kumar5

1  Department of Chemistry and Center for Informatics, Shiv Nadar University, Dadri, India – n.sukumar@snu.edu.in
2  Department of Chemistry, Shiv Nadar University, Dadri, India – ps630@snu.edu.in
3  Dr. Vijay Kumar Foundation, Gurgaon, India – amol_rahane2000@yahoo.com
4  Department of Chemistry, Shiv Nadar University, Dadri, India – ra298@snu.edu.in
5  Dr. Vijay Kumar Foundation, Gurgaon & Center for Informatics, Shiv Nadar University, Dadri, India – vijay.kumar@snu.edu.in

Keywords: electron density analysis, boron clusters, carbon clusters, electron delocalization, structural stability

ABSTRACT


Although the nature of the chemical bond is at the heart of chemistry, chemists often work with several distinct conceptions of the chemical bond, which are not necessarily compatible with each other. The Lewis concept of the electron pair bond [1] is now over a century old, predating the quantum mechanical theory of bonding in molecules. We now recognize electron pairing to be a consequence of the Pauli exclusion principle and the associated Fermi hole. The traditional Lewis electron pair bond concept has been extended to admit the possibility of 3-center, 2-electron bonds in “electron deficient” boranes, and subsequently further extended, using AdNDP analysis [2] (an extension of natural bond orbitals NBO analysis), to include n-center (but always 2-electron) objects (with n arbitrarily large). An alternate to such orbital treatments is provided by examination of topological features of the electron density, such as bond paths (gradient paths of the electron density) connecting pairs of nuclei. Such bond paths are not associated with a fixed electron count. However, as has been pointed out by several authors [3,4], the mere existence of a bond path between a pair of nuclei does not signify the existence of a chemical bond between them or indicate the strength of the interaction. Double integration of the Fermi hole density over spatial regions provides a valid measure of electron localization and delocalization [5]. One can also conceive of the chemical bond as a force that holds a pair of atoms together, quantified by the dissociation energy required to break the bond. While this works well for simple diatomics, the correlation between dissociation energy and electron count or the electron density between a pair of nuclei is not straightforward for open shell systems or polyatomic molecules.
The divergence between these different conceptions of the chemical bond is particularly dramatic for “electron deficient” boron compounds and for metallic nanoclusters, where extensive electron delocalization and multi-center bonding are prevalent. Nevertheless, combining information from topological features of the electron density with orbital-based models allows meaningful chemical conclusions about bonding to be drawn, even for unusual molecular systems.
Here we have analyzed trends in bonding and stability for several clusters including ring-shaped clusters for boron and carbon as well as drum-shaped and fullerene-like clusters of boron, from computed ab initio electron density distributions, and investigated the effects of transition metal (TM) doping on their structural and physical properties. Analysis of the electron density at bond and ring critical points, the Laplacian of the electron density, the electron localization function [6], the source function [7], and localization-delocalization indices, all indicate the coexistence of covalent bonds and delocalized charge distribution in boron clusters [8]. Rings of carbon atoms too seem to be stabilized by metal coordination for selected sizes and electron counts. For drum-shaped M@B14 (M = a 3d TM atom) and M@B16 (M = 3d, 4d, and 5d TM atom) clusters, our results suggest two- and three-center σ bonding within and between two B7/B8 rings, respectively, and hybridization between the TM d orbitals and the π bonded molecular orbitals of the drum. Assembly of Co@B14 clusters has been shown to stabilize a metallic Co atomic nanowire within a boron nanotube [9].
We have also studied metal atom encapsulated fullerene-like boron cage structures and shown that Cr@B20 is the smallest cage for Cr encapsulation, while B22 is the smallest symmetric cage for Mo and W encapsulation. Electron density and molecular orbital analysis suggests that Cr@B18, Cr@B20, M@B22 (M = Cr, Mo, and W) and M@B24 (M = Mo and W) cages are stabilized by 18 p-bonded valence electrons, whereas the drum-shaped M@B18 (M = Mo and W) clusters are stabilized by 20 p-bonded valence electrons [10]. We have also studied larger boron clusters in the size range 68-74 and shown that the global minimum structure for B70 is a tubular structure, which is nearly degenerate with a quasi-planar structure having three hexagonal vacancies [10]. Analysis of a large number of atomic clusters, of various shapes and sizes, indicates a broad parallelism between different measures of bonding and localization in these clusters.

Fig. 1 Electrostatic potential of (a) Cr@B22 and (b) tubular B70 cluster mapped onto a r(r) = 0.1 e/Bohr3 electron density isosurface. Blue regions indicate negative electrostatic potentials associated with the boron atoms. (c) Contour plot of the Laplacian of Cr@B22 cluster in a plane passing through atoms B7, B8, B15, B16, B21, B22, and Cr. Solid (dashed blue) contours indicate positive (negative) values of L = -Ñ2r(r)

Acknowledgements


The authors gratefully acknowledge use of the high-performance computing facility Magus of Shiv Nadar University. ABR and VK thankfully acknowledge financial support from International Technology Center - Pacific. We thank Prof. Cherif Matta for providing access to AIMLDM software.

References


[1] G. N. Lewis, J. Amer. Chem. Soc. 38, 762 (1916).
[2] D. Y. Zubarev, A. I. Boldyrev, Phys. Chem. Chem. Phys. 10, 5207(2008); A. P. Sergeeva, D. Y. Zubarev, H.-J. Zhai, A. I. Boldyrev, L. S. Wang, J. Am. Chem. Soc. 130, 7244 (2008); W. Huang, A. P. Sergeeva, H.-J. Zhai, B. B. Averkiev, L. S. Wang, A. I. Boldyrev, Nat. Chem. 2, 202 (2010).
[3] R. F. W. Bader, Atoms in Molecules: A Quantum Theory, Oxford Press, Oxford (1990).
[4] S. Shahbazian, Chem. Eur. J. (2018) doi:10.1002/chem.201705163
[5] C. F. Matta, J. Comput. Chem. 35, 1165 (2014); M. J. Timm , C. F. Matta, L. Massa, L. Huang, J. Phys. Chem. A, 118, 11304 (2014); C. F. Matta, I. Sumar, R. Cook, P. W. Ayers, In: Applications of Topological Methods in Molecular Chemistry (Challenges and Advances in Computational Chemistry and Physics Series); Chauvin, R.; Silvi, B.; Alikhani, E.; Lepetit, C. (Eds.), Springer, (2015) ; I. Sumar, R. Cook, P. W. Ayers, C. F. Matta, Comput. Theor. Chem. 1070, 55-67 (2015);.I. Sumar, R. Cook, P. W. Ayers, C. F. Matta, Phys. Script. 91, 013001 (2016).
[6] A. D. Becke, K. E. Edgecombe, J. Chem. Phys. 92, 5397 (1990).
[7] R. F. W. Bader, C. Gatti, Chem. Phys. Lett. 287, 233 (1998); C. Gatti, L. Bertini, Acta Cryst. A, 60, 438 (2004); C. Gatti, F. Cargnoni, L. Bertini, J. Comp. Chem. 24, 422 (2003).
[8] P. Saha, A. B. Rahane, V. Kumar, N. Sukumar, Phys. Script. 91, 053005 (2016).
[9] P. Saha, A. B. Rahane, V. Kumar, N. Sukumar, J. Phys. Chem. C, 121, 10728 (2017).
[10] P. Saha, Ph.D. thesis, Shiv Nadar University, India (2018); A.B. Rahane, P. Saha, N. Sukumar, and V. Kumar, to be published.



 

Friday, February 02, 2018

Application of electron density-based analysis in the study of nanoclusters and biomolecular interactions

Related publications
  1. Pinaki Saha, Amol B. Rahane, Vijay Kumar, N. Sukumar, Analysis of the electron density features of small boron ring clusters and the effects of doping, Physica Scripta 91, 053005 (2016). DOI: 10.1088/0031-8949/91/5/053005 IF: 1.126
  2. Pinaki Saha, Amol B. Rahane, Vijay Kumar, and N. Sukumar, Electronic Origin of the Stability of Transition Metal Doped B14 Drum Shaped Boron Clusters and Their Assembly in to a Nanotube. J. Phys. Chem. C, 121(20), 10728–10742 (2017). DOI: 10.1021/acs.jpcc.6b10838 IF: 4.536
  3. Suman Kumar Mandal, Pinaki Saha, Parthapratim Munshi, N. Sukumar, Exploring Potent Ligand for Proteins: Insights from Knowledge-based Scoring Functions and Molecular Interaction Energies, Struct. Chem. 28(5), 1537-1552 (2017). DOI: 10.1007/s11224-017-1007-y
  4. Amol B. Rahane, Pinaki Saha, N. Sukumar and Vijay Kumar, Smallest Fullerene-like Structures of Boron with Cr, Mo, and W Encapsulation (manuscript under review).

Thursday, June 29, 2017

PhD Defense of Mr. Ganesh Prabhu, Department of Chemistry, Shiv Nadar University



PhD Defense of Mr. Ganesh Prabhu
Department of Chemistry, Shiv Nadar University

DATE:                    Monday, July 3, 2017

TIME:                    1:30 PM – 2:30 PM    

LOCATION:         D-217 (Seminar Hall -2)

TITLE:    Diversity-Oriented Synthesis of A Molecular Library, Analysis of Molecular Diversity Through Network/Graph Measures and Correlation with Biological Activity Profile(s)

ADVISOR:            Dr. N Sukumar and Dr. Subhabrata Sen
ABSTRACT: In this thesis, we examined the concepts of molecular similarity / dissimilarity and the growth of Diversity Oriented Synthesis (DOS) as an alternative to combinatorial chemistry. A brief description of the procedures for the synthesis of hybrid compounds using DOS via “Platform technology” is followed by detailed experimental analysis for synthesizing natural product inspired hybrids using the pyrroloisoquinoline scaffold as a platform. The hybrids were screened against several phenotypes for cytotoxicity, antiplasmodial activity and anti-malarial activity. A cheminformatics study was undertaken for comparing the physico-chemical properties of the synthesized hybrids against similar commercial drugs. We also studied the dissimilarity / similarity features of the chemical libraries through an analysis of the topological properties of threshold Chemical Space Networks (CSNs). During the process, we developed QuaLDI (Quantitative Library Diversity Index), a simple measure for quantifying diversity in DOS, Focussed and PubChem Libraries. The effectiveness of QuaLDI was evaluated by comparing the results from QuaLDI with other diversity measures. We used correlation matrix guided approach combined with QuaLDI measure as an effective approach for selecting minimally correlated descriptors for QSAR modelling.