Journal of Computational Biophysics and Chemistry

Almost all biological reactions are pH-dependent and understanding the origin of pH dependence requires knowledge of the pKa’s of ionizable groups. Here, we report a new edition of PKAD, the PKAD-2, which is a database of experimentally measured pKa’s of proteins, both wild-type and mutant proteins. The new addition includes 117 wild-type and 54 mutant pKa values, resulting in total 1742 experimentally measured pKa’s. The new edition of PKAD-2 includes eight new wild-type and 12 new mutant proteins, resulting in total of 220 proteins. This new edition incorporates a visual 3D image of the highlighted residue of interest within the corresponding protein or protein complex. Hydrogen bonds (HB) were identified, counted and implemented as a search feature. Other new search features include the number of neighboring $\mathrm{\text{residues}}<4$ Å from the heaviest atom of the side chain of a given amino acid. Here, we present PKAD-2 with the intention to continuously incorporate novel features and current data with the goal to be used as a benchmark for computational methods.

The COVID-19 pandemic raised an unprecedented race in biotechnology in search for effective therapies and a preventive vaccine. Scientists worldwide have been attempting to stop the viral infection by interfering with the biological function of the SARS-CoV-2 main protease (Mpro), a critical protein required for viral transcription and replication during infection. In this study, we employed an effective approach integrating deep learning model calculations and steered molecular dynamic simulations to generate highly promising inhibitors of SARS-CoV-2 Mpro. First, using deep learning calculations, a natural molecule that was identified as a potential inhibitor of SARS-CoV-2 Mpro was chemically altered to boost its ligand-binding affinity to the Mpro protease. The proposed compounds were then verified using steered molecular dynamic simulations to estimate their binding free energies to SARS-CoV-2 Mpro. The procedure was repeated until the binding free energies of the proposed compounds did not improve further. Overall, one proposed compound was shown to have a high nanomolar affinity, and two others were estimated to possess nanomolar affinities for SARS-CoV-2 Mpro, indicating that they are highly promising inhibitors of the protease. Absorption, distribution, metabolism, and excretion and toxicity analysis show that all three chemicals are drug-like compounds following the MACCS-II Drug Data Report database, orally absorbed, tightly attached to the plasma membrane, and noncarcinogenic in rats. The results obtained potentially support COVID-19 treatment.

In molecular dynamics simulations of membrane systems, it is very important to evaluate the interactions between membrane bilayers and other molecules like small compounds, peptides, or proteins. When these interactions lead to local deformations in the membrane, it is essential to fully understand and accurately characterize them, since these deformations can impact the calculation of membrane properties. Here, we introduce MembIT, a simple standalone tool that calculates several membrane properties that are very useful to gauge system equilibration and quality control in molecular dynamics simulations. It has implemented a distinction between local and bulk lipids, which corrects the local deformations, that are often overlooked by other tools. We show examples of MembIT calculations of membrane insertion and deformation values in systems containing POPC and a small drug (sunitinib), a transmembrane peptide (pHLIP), and a transmembrane protein (ADP/ATP carrier). We hope that this tool will be adopted by the scientific community and that the analyses that it provides become good practices in MD simulations of membrane systems.

The capability and potential of C${}_{24}$, ScC${}_{23}$, TiC${}_{23}$, and NiC${}_{23}$ nanocages as novel candidates for delivery and sensor property of the Prothionamide (PA) drug in a biological system are investigated with density functional theory. The adsorption energy and thermodynamic parameters of PA@C${}_{24}$, PA@ScC${}_{23}$, PA@TiC${}_{23}$, and PA@NiC${}_{23}$ complexes in the absences and presence of static electrical field (SEF) (SEF${}_{z+0.01}$, SEF${}_{z+0.02}$, SEF${}_{z+0.03}$, and SEF${}_{z+0.04}$ a.u.) are calculated, and results indicated that the $E_{\mathrm{\text{ads}}}$, $\mathrm{\Delta }H$, and $\mathrm{\Delta }G$ values for all studied complexes in gas media are negative and exothermic. In the presence of water solvent, the $\mathrm{\Delta }\mathrm{\Delta }{G}_{(\mathrm{\text{sol}})}$ values of all drug and nanocage complexes are positive. The quantum descriptors, molecular electrostatic potential, the density of state, UV–visible spectrum, and dipole moment of all drug and nanocage complexes are determined and results are analyzed. The topological results of atom in molecule and the noncovalent interaction index display that the interaction of PA drug with C${}_{24}$ is electrostatic- and van der Waals-type. The computational results suggest that the ScC${}_{23}$, TiC${}_{23}$, and NiC${}_{23}$ nanocages can be used as good candidates for the delivery and sensor of the PA drug.

Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has limitations, including its inability to handle heterogeneous information, such as multiple types of geometric objects; being qualitative rather than quantitative, e.g., counting a 5-member ring the same as a 6-member ring, and a failure to describe nontopological changes, such as homotopic changes in protein–protein binding. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the limitations of persistent homology. In this work, we examine the modeling and analysis power of PTLs in the study of the protein structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor binding domain (RBD). First, we employ PTLs to study how the RBD mutation-induced structural changes of RBD-angiotensin-converting enzyme 2 (ACE2) binding complexes are captured in the changes of spectra of the PTLs among SARS-CoV-2 variants. Additionally, we use PTLs to analyze the binding of RBD and ACE2-induced structural changes of various SARS-CoV-2 variants. Finally, we explore the impacts of computationally generated RBD structures on a topological deep learning paradigm and predictions of deep mutational scanning datasets for the SARS-CoV-2 Omicron BA.2 variant. Our results indicate that PTLs have advantages over persistent homology in analyzing protein structural changes and provide a powerful new TDA tool for data science.

Lysosomal storage diseases (LSDs) consist of about 60 different monogenic disorders. Most of them occur due to protein misfolding. Only a few of those have been treated with molecular chaperones; the remaining either have limited treatment options or only management therapies. About 1860 single amino-acid substitutions (SAS) have been identified under LSDs. Merely, a handful of mutations have been studied experimentally. Availability of computational tools has made researchers turn toward genetic disorders to focus light on unexplored disorders and their mutations. Since all the LSDs are rare genetic disorders, not much research is carried out in this area. However, a mutational effect on protein function could be predicted, through bioinformatics tools. On that note, out of 1860 SAS, 58 predictions were neutral and 778 were predicted to be disease associated by all programs included in this study. The result of the prediction analysis of all mutations in each of the LSDs is curated into a database. This would make researchers know the deleterious nature of a mutation causing LSD. The database is available at http://lsddb.vit.ac.in:3000/.

Human orthopneumovirus (OPV) is a common respiratory virus that mostly causes respiratory illness in children. Development of therapeutic strategies to treat OPV infection has attracted considerable interest in the medicinal and biological communities. Over the past decades, significant effects have been addressed on disrupting membrane fusion events by targeting viral fusogenic glycoprotein (F-protein) with chemical small-molecule fusion inhibitors. Here, we focus on the rational design of biologic peptide inhibitors to disrupt the peptide-mediated interaction (PMI) between phosphoprotein (P-protein) and nucleoprotein (N-protein) involved in OPV genomic ribonucleoprotein complex. It is revealed that the core 9-mer peptide segment (C-peptide) derived from the C-terminal tail of P-protein is intrinsically disordered in an unbound state but would be structured into an ordered helical conformation when bound to N-protein, representing a biophysical process known as coupled folding-upon-binding. We demonstrated that the process is a compromise between the favorable enthalpy contribution and unfavorable entropy penalty, thus rendering the weak and transient nature of PMI, which could therefore be readily disrupted by competitive agents. Hydrocarbon stapling strategy was used to constrain the helical conformation of C-peptide in an unbound state by chemically introducing an all-hydrocarbon bridge spanning two $[i,i+4]$ residues of C-peptide. The stapling is observed to considerably enhance the helical propensity of peptide in an unbound state and to effectively improve the binding affinity of peptide to N-protein. Structural modeling analysis suggests that the stapled counterparts can bind to the active site of N-protein in a similar mode with unstapled C-peptide, where the stapled peptide is folded into a helical-like conformation, its anchor residue Phe241 deeply roots in a small druggable pocket of the active site, and the all-hydrocarbon bridge, as designed, is out of N–C complex interface to avoid disrupting the complex interaction.

To study the three-dimensional quantitative structure–activity relationship (3D-QSAR) of a series of GPR52 agonists, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were used to establish 3D-QSAR models. The cross-validation coefficient ($q^2$), non-cross validation coefficient ($r^2$) and standard error of estimate (SEE) of the constructed CoMFA model were 0.679, 0.981 and 0.058, respectively. While those for the CoMSIA model were 0.661, 0.846 and 0.110, respectively. The internal and external validation parameters showed that the constructed 3D-QSAR models had good prediction ability and significant statistical reliability. According to the information provided by 3D-QSAR analysis, new compounds were designed, and the molecular docking together with absorption, distribution, metabolism, exclusion, toxicity (ADMET) prediction results showed that the new compounds had good GPR52 agonistic activity.