Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS

An integrated multiscale computational framework deciphers SARS-CoV-2 resistance to sotrovimab

The emergence of resistance mutations in the SARS-CoV-2 spike (S) protein presents a challenge for monoclonal antibody treatments like sotrovimab. Understanding the structural, dynamic, and molecular features of these mutations is essential for therapeutic advancements. However, the intricate landscape of potential mutations and critical residues conferring resistance to mAbs like sotrovimab remains elusive. This study introduces an integrated framework that combines interface protein design, machine learning, hybrid quantum mechanics/molecular mechanics methodologies, all-atom and coarse-grained molecular dynamics simulations, and correlation analysis. Beginning with the interface-based design and analysis, this framework elucidates the interaction between sotrovimab and the S-protein, identifying pivotal residues and plausible resistance mutations. Machine learning algorithms then facilitate the identification of potential resistance mutations using structural-sequence-binding affinity-energetics features. The hybrid quantum mechanics/molecular mechanics approach subsequently evaluates the role of mutational residues as quantum regions, assessing their impact on stabilizing the macromolecular complex. To gain a deeper understanding of the dynamic behavior of these mutations, multiscale simulations comprising all-atom and coarse-grained molecular dynamics simulations were performed, revealing their structural, biophysical and energetic impacts. These simulations complemented the static predictions, capturing the conformational dynamics and stability of the mutants in presence of glycan in the S-protein. The accuracy of the predictions is validated by correlating identified resistance mutations with clinical-sequencing data and empirical evidence from sotrovimab-treated patients. Notably, two residues, E340 at the S-protein-sotrovimab interface and Y508 distal from it, and their designs, align with clinically observed resistance mutations. Furthermore, machine learning approaches predict novel S-protein sequences with enhanced/reduced affinity for sotrovimab, validated structurally using AlphaFold. This integrated framework showcases its effectiveness in identifying potential resistance mutations, corroborated with clinical insights and offering a multidimensional strategy for decoding resistance mutations in SARS-CoV-2. Its translational relevance extends to understanding resistance mechanisms and designing novel antibody therapeutics in other systems.

Comparative analysis of backbone atom cross-correlation matrices and folding dynamics of amyloid fibril and its complexes with novel biosurfactants isolated from Bacillus strain: a binding free energy calculation (mM-PBSA) and MD simulation approach

In the relentless pursuit of unraveling the intricate pathophysiology of Alzheimer's disease (AD), amyloid β (Aβ) proteins emerge as focal points due to their pivotal role in disease progression. The pathological hallmark of AD involves the aberrant aggregation of Aβ peptides into amyloid fibrils, precipitating a cascade of neurodegenerative events culminating in cognitive decline and neuronal loss. This study adopts a computational framework to investigate the potential therapeutic efficacy of novel biosurfactants (BS) in mitigating Aβ fibril formation. Initial analyses encompassing sequence alignment, structural elucidation, and functional characterization reveal distinctive attributes of the Aβ peptide and the identified BS candidates. Quantum chemical calculations, using the ORCA Program (v4.0) employed Density Functional Theory (DFT), specifically the Becke 3-parameter Lee-Yang-Parr (B3LYP) method, to investigate the electronic structure and energetics of novel isolates. Molecular docking through AutoDock Vina (version 1.1.2) employing advanced algorithms elucidates the binding affinities and interaction energies between Aβ fibrils and BS molecules. The observed binding energy of -7.0 kcal/mol for BG2A and -6.6 kcal/mol for BG2B, underscoring the robustness and stability of the formed complexes. The binding mechanism of docked complexes was predicted through molecular dynamics (MD) simulations using GROMACS 2021.3 and Charmm36 force field, capture complex dynamics over 100 nanoseconds. Analysis via RMSD, RMSF, Rg, PCA, and SASA offers insights into Aβ-BS complex stability and dynamics. These promising results highlight the potential of BG2A and BG2B as therapeutic candidates against AD. However, rigorous preclinical and clinical validation is crucial to ascertain their safety, efficacy, and translational relevance.

Integrated all-atom and coarse-grained simulations uncover structural, dynamics and energetic shifts in SARS-CoV-2 JN.1 and BA.2.86 variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is an enveloped, positive-stranded RNA virus that enters human cells by using its spike protein to bind to the human angiotensin-converting enzyme 2 (ACE2) receptor. Since its emergence, the virus has mutated, producing variants with increased transmissibility, immune evasion, and infectivity. The JN.1 variant, detected in January 2024, features a single substitution mutation (Leu455Ser) in the receptor-binding domain (RBD) of its spike protein, setting it apart from its parent lineage, BA.2.86. This variant has rapidly become globally predominant due to its enhanced transmission and significant epidemiological impact. To understand the causes behind the dominance of the JN.1 variant, we conducted a comprehensive study using all-atom molecular dynamics (MD) and coarse-grained MD simulations. This allowed us to examine the structural, dynamic, energetics and binding properties of the wild-type (Wuhan strain), BA.2.86, and JN.1 variants. Principal component and free energy landscape analyses revealed enhanced structural stability in the JN.1 variant. Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) assessments indicated lower binding affinity for JN.1 as compared to BA.2.86. Intermolecular interaction analyses further confirmed BA.2.86's superior binding affinity over JN.1 and wild-type. Additionally, we compared and validated our findings against experimentally determined cryo-electron microscopy (cryo-EM) structures of JN.1 and BA.2.86 variants, confirming the reliability of our simulation results. Overall, this study provides crucial insights into the structural-dynamics-energetics features and physicochemical properties that have contributed to the global prevalence of the JN.1 variant and sheds light on its potential to generate future subvariants.