Biology subjects

Panda, P. K.

Publications and source records attributed to Panda, P. K..

7 recordsLinked to original sources

bioRxiv2026

PandaDock: An Open-Source Molecular Docking Platform with Flexible-Ligand Search and Equivariant Neural Scoring

We present PandaDock, an open-source molecular docking platform implementing flexible-ligand conformational search with analytic gradients, a precomputed affinity grid engine, specialized modules for induced-fit, metal-coordination and tethered docking, and an SE(3)-equivariant graph neural network scoring function trained at scale. Ligand flexibility is represented as a torsion tree and pose parameters are optimized by Monte Carlo with Metropolis acceptance refined by L-BFGS, with rotational gradients obtained in closed form through the derivative of the SO(3) exponential map rather than by finite differences. Affinity grids are built by a blocked neighbor-selection scheme that is exact and 5.6-9.7x faster than dense evaluation, and may be cached across ligands sharing a receptor and site, reducing a six-ligand series from 29.3 s to 10.4 s. On 814 protein-ligand complexes spanning 14 target families, PandaDock recovers a pose within 2 Angstroms of the crystal geometry in 33.7% of cases at rank 1 and in 57.0% of cases within the returned ensemble. The GNN scoring function is trained on 741,706 co-folded complexes from SAIR under target-disjoint splits, reaching a Pearson r of 0.407 on 90,219 held-out complexes and transferring to 202 independent crystal structures with measured Ki, Kd, IC50 or EC50 at r = 0.467. We report the model against three controls, a target-mean predictor, a ligand-descriptor-only baseline, and within-target correlations, and document both where it performs and where it does not, including its unsuitability for pose rescoring. On an independent 30-compound series against a single GABAA receptor target, PandaDock's empirical scoring function ranks 8th of 25 methods evaluated, ahead of every AutoDock Vina and Vinardo configuration tested, while the GNN scores below Vina, consistent with the within-target ceiling identified on SAIR. At full scale on the PDBbind v2020 refined set (n = 4,640, native crystal poses), the fully independent SAIR model reaches r = 0.531, and a dedicated model trained on PDBbind alone under a target-disjoint split reaches r = 0.690 on its own held-out test complexes, the strongest evidence in this work that PandaDock's affinity predictions generalize. PandaDock is distributed under an open-source license at https://github.com/pritampanda15/PandaDock with a complete command-line interface and a reproducible benchmarking harness.

bioinformatics↗

bioRxiv2026

Structural Context Determines Docking Engine Performance: A Family-Stratified Benchmark of Six Engines

Molecular docking and co-folding engines are widely used to prioritize compounds for wet-lab validation, yet their accuracy is known to vary substantially across protein targets for reasons that remain only qualitatively understood. Here we benchmark six docking and co-folding engines (RevDock, DiffDock, Boltz2, AutoDock-GPU, rDock, and PandaDock) across 14 protein families, evaluating scoring power, ranking power, docking power, and physical validity. Rather than treating engine performance as protein-family-specific, we classify all 14 families into six mechanistic groups according to which of four scoring-function simplifications, rigid receptor, pairwise additivity, fixed point charges, and implicit solvent, is most severely stressed by that familys binding site. This framework helps explain, rather than simply describe, where each engine succeeds or fails: RevDocks CNN rescoring layer mitigates the pairwise additivity and fixed-charge limitations relative to physics-only scoring, achieving the highest overall pose accuracy (73.3% of poses [≤] 2.0 [A] RMSD), while Boltz2s sequence-based co-folding bypasses the rigid-receptor assumption and achieves comparable affinity correlation (mean Pearson r {approx} 0.60 for both engines). PandaDock, run with expanded conformational sampling, matches RevDock on pose accuracy (72.1% of poses [≤] 2.0 [A], lowest median RMSD at 0.96 [A]) and exceeds AutoDock-GPU on affinity correlation (mean r = 0.460), indicating that the performance of a physics-based scoring function is limited as much by search adequacy as by the scoring function itself. These results suggest that engine selection for a docking or co-folding campaign should be guided less by an engines aggregate benchmark ranking and more by which of these four structural and physical characteristics dominate the target of interest.

biochemistry↗

bioRxiv2026

PandaMap: A Python Package for Comprehensive Visualization of Protein-Ligand Interaction Networks

Protein-ligand interaction diagrams are a routine part of structural and medicinal chemistry, but the tools that produce them tend to force a choice: comprehensive detection with tabular output, publication-quality figures behind a licence, or a scripting environment that assumes expertise. PandaMap (Protein AND ligAnd interaction MAPper) is an open-source Python package that produces a 2D interaction diagram, an interactive 3D viewer, a text report, a machine-readable CSV, and a four-panel graphical summary from a single command. It reads PDB, mmCIF and PDBQT files, detects 15 interaction classes using crystallographically validated distance thresholds, and depends only on NumPy, Matplotlib, BioPython and Requests; RDKit improves the 2D ligand layout when present but is not required. Hydrogen bonds are filtered on the true D-H{middle dot} {middle dot} {middle dot} A angle when the structure contains explicit hydrogens, matching PLIPs 100{whitebullet} criterion on the same evidence, and on distance alone otherwise, with the provenance of each measurement recorded. We benchmarked the package on three complexes chosen for different chemistry: enolase with a phosphonate transition-state analogue (PDB 1ELS), the EGFR kinase with erlotinib (1M17), and aldose reductase with IDD594 (1US0). PandaMap recovers the contacts these structures are known for, including the EGFR hinge hydrogen bond to MET769 and the IDD594 bromine{middle dot} {middle dot} {middle dot} THR113 halogen bond, both at distances identical to PLIPs. All detection thresholds, scoring weights and the exact commands used are given in the Supplementary Information, and the release carries a regression suite covering each interaction class. PandaMap 4.3.0 is available on PyPI under the MIT licence.

bioinformatics↗

bioRxiv2024

Disruption of molecular interactions between G3BP1 stress granule host protein and nucleocapsid (NTD-N) protein impedes SARS-CoV-2 virus replication

The Ras GTPase-activating protein SH3-domain-binding protein 1 (G3BP1) serves as a formidable barrier to viral replication by generating stress granules (SGs) in response to viral infections. Interestingly, viruses, including SARS-CoV-2, have evolved defensive mechanisms to hijack SG proteins like G3BP1 for the dissipation of SGs that lead to the evasion of hosts immune responses. Previous research has demonstrated that the interaction between the NTF2-like domain of G3BP1 (G3BP1NTF-2) and the intrinsically disordered N-terminal domain (NTD-N1-25) of the N protein plays a crucial role in regulating viral replication and pathogenicity. Interestingly, the current study identified an additional upstream stretch of residues (128KDGIIWVATEG138) (N128-138) within the N-terminal domain of the N protein (NTD-N41-174) that also forms molecular contacts with the G3BP1 protein, as revealed through in silico analysis, site-directed mutagenesis and biochemical analysis. Remarkably, WIN-62577, and fluspirilene, the small molecules targeting the conserved peptide binding pocket in G3BP1NTF-2, not only disrupted the protein-protein interactions (PPIs) between the NTD-N41-174 and G3BP1NTF-2 but also exhibited significant antiviral efficacy against SARS-CoV-2 replication with EC50 values of [~]1.8 {micro}M and [~]1.3 {micro}M, respectively. The findings of this study, validated by biophysical thermodynamics and biochemical investigations, advance the potential of developing therapeutics targeting the SG host protein against SARS-CoV-2, which may also serve as a broad-spectrum antiviral target.

biochemistry↗

bioRxiv2024

Residual Microglia Following Short-term PLX5622 Treatment in 5xFAD Mice Exhibit Diminished NLRP3 Inflammasome and mTOR Signaling, and Enhanced Autophagy

Chronic neuroinflammation represents a prominent hallmark of Alzheimers disease (AD). While moderately activated microglia are pivotal in clearing amyloid beta (A{beta}), hyperactivated microglia perpetuate neuroinflammation. Prior investigations have indicated that the elimination of [~]80% of microglia through a month-long inhibition of the colony-stimulating factor 1 receptor (CSF1R) during the advanced stage of neuroinflammation in 5xFamilial AD (5xFAD) mice mitigates synapse loss and neurodegeneration without impacting A{beta} levels. Furthermore, prolonged CSF1R inhibition diminished the development of parenchymal plaques. Nonetheless, the immediate effects of short-term CSF1R inhibition during the early stages of neuroinflammation on residual microglial phenotype or metabolic fitness are unknown. Therefore, we investigated the effects of 10-day CSF1R inhibition in three-month-old female 5xFAD mice, a stage characterized by the onset of neuroinflammation and minimal A{beta} plaques. We observed [~]65% microglia depletion in the hippocampus and cerebral cortex. The leftover microglia demonstrated a noninflammatory phenotype, with highly branched and ramified processes and reduced NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome complexes. Moreover, plaque-associated microglia were reduced in number with diminished Clec7a (dectin-1) expression. Additionally, both microglia and neurons displayed reduced mechanistic target of rapamycin (mTOR) signaling and autophagy. Biochemical assays validated the inhibition of NLRP3 inflammasome activation, decreased mTOR signaling, and enhanced autophagy. However, short-term CSF1R inhibition did not influence A{beta} plaques, soluble A{beta}-42 levels, or hippocampal neurogenesis. Thus, short-term CSF1R inhibition during the early stages of neuroinflammation in 5xFAD mice promotes the retention of homeostatic microglia with diminished inflammasome activation and mTOR signaling, alongside increased autophagy.

neuroscience↗

bioRxiv2023

Unraveling antiviral efficacy of multifunctional immunomodulatory triterpenoids against SARS-COV-2 targeting virus-specific enzymes

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be over, but its variants continue to emerge, and patients with mild symptoms having long COVID is still under investigation. SARS-CoV-2 infection leading to elevated cytokine levels and suppressed immune responses set off cytokine storm, fatal systemic inflammation, tissue damage, and multi-organ failure. Thus, drug molecules targeting the SARS-CoV-2 virus-specific proteins or capable of suppressing the host inflammatory responses to viral infection would provide an effective antiviral therapy against emerging variants of concern. Evolutionarily conserved papain-like protease (PLpro) and main protease (Mpro) play an indispensable role in the virus life cycle and immune evasion. Direct-acting antivirals targeting both these viral proteases represent an attractive antiviral strategy that is also expected to reduce viral inflammation. The present study has evaluated the antiviral and anti-inflammatory potential of natural triterpenoids: azadirachtin, withanolide_A, and isoginkgetin. These molecules inhibit the Mpro and PLpro proteolytic activities with half-maximal inhibitory concentrations (IC50) values ranging from 1.42 to 32.7 {micro}M. Isothermal titration calorimetry (ITC) analysis validated the binding of these compounds to Mpro and PLpro. As expected, the two compounds, withanolide_A and azadirachtin, exhibit potent anti-SARS-CoV-2 activity in cell-based assays, with half- maximum effective concentration (EC50) values of 21.73 {micro}M and 31.19 {micro}M, respectively. The anti-inflammatory role of azadirachtin and withanolide_A when assessed using HEK293T cells were found to significantly reduce the levels of CXCL10, TNF, IL6, and IL8 cytokines, which are elevated in severe cases of COVID-19. Interestingly, azadirachtin and withanolide_A were also found to rescue the decreased type-I interferon response (IFN-1). The results of this study clearly highlight the role of triterpenoids as effective antiviral molecules that target SARS-CoV-2 specific enzymes and also host immune pathways involved in virus mediated inflammation.

molecular biology↗

bioRxiv2022

Network analysis of chromophore binding site in LOV domain

Photoreceptor proteins are versatile toolbox for developing biosensors for optogenetic applications. These molecular tools get activated upon illumination of blue light, which in turn offers a non-invasive method for gaining high spatiotemporal resolution and precise control of cellular signal transduction. The Light-Oxygen-Voltage (LOV) domain family of proteins is a well-recognized system for constructing optogenetic devices. Translation of these proteins into efficient cellular sensors is possible by tuning their photochemistry lifetime. However, the bottleneck is the need for more understanding of the relationship between the protein environment and photocycle kinetics. Significantly, the effect of the local environment also modulates the electronic structure of chromophore, which perturbs the electrostatic and hydrophobic interaction within the binding site. This work highlights the critical factors hidden in the protein networks, linking with their experimental photocycle kinetics. It presents an opportunity to quantitatively examine the alternation in chromophores equilibrium geometry and identify details which have substantial implications in designing synthetic LOV constructs with desirable photocycle efficiency.

bioengineering↗