@article {43878, title = {Mixed-stack charge transfer crystals of pillar[5]quinone and tetrathiafulvalene exhibiting ferroelectric features}, journal = {Chemistry- A European Journal }, volume = {23}, year = {2017}, month = {SEP}, chapter = {12630-12635}, abstract = {

Ferroelectric materials find extensive applications in the fabrication of compact memory devices and ultra-sensitive multifunctional detectors. Face-to-face alternate stacking of electron donors and acceptors effectuate long-range unidirectional ordering of charge-transfer (CT) dipoles, promising tunable ferroelectricity. Herein we report a new TTF-quinone system-an emerald green CT complex consisting pillar[5]quinone (P5Q) and tetrathiafulvalene (TTF). The CT crystals, as determined by single crystal synchrotron X-ray diffraction, adopt a 1:1 mixed-stack arrangement of donor and acceptor with alternating dimers of TTF and 1,4-dioxane encapsulated P5Q. The TTF-P5Q.dioxane crystal possesses a macroscopic polarization axis giving rise to ferroelectricity at room temperature. The CT complex manifests ferroelectric features such as optical polarization rotation, temperature-dependent phase transition and piezoelectric response in single crystals. Ferroelectric behavior observed in P5Q-based CT complex widens the scope for further work on this structurally intriguing and readily accessible cyclic pentaquinone.

}, keywords = {Acceptor, Charge-transfer complexes, Chemistry, Conductors, DDQ, Donor, ferroelectric, Macrocycles, Organic Metals, Pi-interaction, Pillar[5]quinone, salts, Transfer Complexes, Transport}, issn = {0947-6539}, doi = {10.1002/chem.201702577}, author = {Shivakumar, Kilingaru I. and Swathi, Kadaba and Goudappagouda and Das, Tamal C. and Kumar, Ashwani and Makde, Ravindra D. and Vanka, Kumar and Narayan, Kavassery S. and Babu, Sukumaran Santhosh and Sanjayan, Gangadhar J.} } @article {47392, title = {Machine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics}, journal = {Bioorganic \& Medicinal Chemistry Letters}, volume = {38}, year = {2021}, month = {APR }, pages = {127852}, type = {Article}, abstract = {

De novo design of mini-proteins (4-12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. In this study we trained machine learning classifiers which can distinguish, with 90\% accuracy and 80\% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.

}, keywords = {Drug design, machine learning, Mini-proteins, Protein therapeutics, virtual screening}, issn = {0960-894X}, doi = {10.1016/j.bmcl.2021.127852}, author = {Gaur, Neeraj K. and Goyal, Venuka Durani and Kulkarni, Kiran and Makde, Ravindra D.} } @article {49133, title = {Evolutionary conservation of protein dynamics: insights from all-atom molecular dynamics simulations of {\textquoteleft}peptidase{\textquoteright} domain of Spt16}, journal = {Journal of Biomolecular Structure and Dynamics}, volume = {41}, year = {2023}, month = {MAR }, pages = {1445-1457}, type = {Article}, abstract = {

Protein function is encoded in its sequence, manifested in its three-dimensional structure, and facilitated by its dynamics. Studies have suggested that protein structures with higher sequence similarity could have more similar patterns of dynamics. However, such studies of protein dynamics within and across protein families typically rely on coarse-grained models, or approximate metrics like crystallographic B-factors. This study uses mu s scale molecular dynamics (MD) simulations to explore the conservation of dynamics among homologs of similar to 50 kDa N-terminal module of Spt16 (Spt16N). Spt16N from Saccharomyces cerevisiae (Sc-Spt16N) and three of its homologs with 30-40\% sequence identities were available in the PDB. To make our data-set more comprehensive, the crystal structure of an additional homolog (62\% sequence identity with Sc-Spt16N) was solved at 1.7 angstrom resolution. Cumulative MD simulations of 6 mu s were carried out on these Spt16N structures and on two additional protein structures with varying degrees of similarity to it. The simulations revealed that correlation in patterns of backbone fluctuations vary linearly with sequence identity. This trend could not be inferred using crystallographic B-factors. Further, normal mode analysis suggested a similar pattern of inter-domain (inter-lobe) motions not only among Spt16N homologs, but also in the M24 peptidase structure. On the other hand, MD simulation results highlighted conserved motions that were found unique for Spt16N protein, this along with electrostatics trends shed light on functional aspects of Spt16N. Communicated by Ramaswamy H. Sarma.

}, keywords = {Backbone fluctuations, FACT complex, histone binding, interdomain motion, M24 peptidase}, issn = {0739-1102}, doi = {10.1080/07391102.2021.2021990}, author = {Gaur, Neeraj K. and Ghosh, Biplab and Goyal, Venuka Durani and Kulkarni, Kiran and Makde, Ravindra D.} } @article {49386, title = {Cell based assay using virus-like particles to screen AM type mimics for SARS-CoV-2 neutralisation}, journal = {BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS}, volume = {718}, year = {2024}, month = {JUL }, pages = {150082}, type = {Journal Article}, keywords = {MINIPROTEINS}, issn = {0006-291X}, doi = {10.1016/j.bbrc.2024.150082}, author = {Gaur, Neeraj Kailash and Urankar, Shreegauri and Sengupta, Durba and Chepuri, V. Ramana and Makde, Ravindra D. and Kulkarni, Kiran} } @article {49439, title = {Design of human ACE2 mimic miniprotein binders that interact with RBD of SARS-CoV-2 variants of concerns}, journal = {Journal of Biomolecular Structure \& Dynamics}, year = {2024}, month = {JAN}, type = {Article; Early Access}, abstract = {

The world of medicine demands from the research community solutions to the emerging problem of SARS-CoV-2 variants and other such potential global pandemics. With advantages of specificity over small molecule drugs and designability over antibodies, miniprotein therapeutics offers a unique solution to the threats of rapidly emerging SARS-CoV-2 variants. Unfortunately, most of the promising miniprotein binders are de novo designed and it is not viable to generate molecules for each new variant. Therefore in this study, we demonstrate a method for design of miniprotein mimics from the interaction interphase of human angiotensin converting enzyme 2 (ACE2). ACE2 is the natural interacting partner for the SARS-CoV-2 spike receptor binding domain (RBD) and acts as a recognition molecule for viral entry into the host cells. Starting with ACE2 N-terminal triple helix interaction interphase, we generated more than 70 miniprotein sequences. Employing Rosetta folding and docking scores we selected 10 promising miniprotein candidates amongst which 3 were found to be soluble in lab studies. Further, using molecular mechanics (MM) calculations on molecular dynamics (MD) trajectories we test interaction of miniproteins with RBD from various variants of concern (VOC). Presently, we report two key findings; miniproteins in this study are generated using less than 10 lab testing experiments, yet when tested through in-vitro experiments, they show submicro to nanomolar affinities towards SARS-CoV-2 RBD. Also in simulation studies, when compared with previously developed therapeutics, our miniproteins display remarkable ability to mimic ACE2 interphase; making them an ideal solution to the ever evolving problem of VOCs.

}, keywords = {ACE2 mimics, miniprotein, protein design, SARS-CoV-2, therapeutics, Variant of concern}, issn = {0739-1102}, doi = {10.1080/07391102.2024.2310789}, author = {Gaur, Neeraj K. and Khakerwala, Zeenat and Makde, Ravindra D.} }