Pregnane X receptor (PXR) is a heterologous biosensor that is involved in the metabolic pathway of environmental pollutants, regulating the transcription of genes involved in biotransformation. There are significant differences in the selectivity and specificity of organic pollutants (OPs) toward polar bear PXR (pbPXR) and human PXR (hPXR), but the detailed dynamical characteristics of their interactions are unclear. Homology Modeling, molecular docking, molecular dynamics simulation, and free energy calculation were used to analyze the recognition of pbPXR and hPXR by three OPs: BPA, chlordane and toxaphene. Comparing interaction patterns along with binding free energy of pbPXR and hPXR with these three OPs revealed that although pbPXR and hPXR interact similar with these three OPs, these OPs have different effects on the internal dynamics of pbPXR and hPXR. This results in significant alterations in the interaction of key residues near Leu209, Met243, Phe288, Met323, and His407 with OPs, thereby influencing their binding energy. Non-polar interactions, especially van der Waals interactions, were found to be the dominating factors in interacting of these OPs with PXRs. The region surrounding these key residues facilitates hydrophobic contacts with PXR, which are crucial for the selective activation of PXRs in different species by these three OPs. These findings are of significant guidance in understanding the impacts of environmental endocrine disruptors on different organisms.

The polysialyltransferases ST8SIA2 and ST8SIA4 and their product, polysialic acid (polySia), are known to be related to cancers and mental disorders. ST8SIA2 and ST8SIA4 have conserved amino acid (AA) sequence motifs essential for the synthesis of the polySia structures on the neural cell adhesion molecule. To search for a new motif in the polysialyltransferases, we adopted the in silico Individual Meta Random Forest program that can predict disease-related AA substitutions. The Individual Meta Random Forest program predicted a new eight-amino-acids sequence motif consisting of highly pathogenic AA residues, thus designated as the pathogenic (P) motif. A series of alanine point mutation experiments in the pathogenic motif (P motif) showed that most P motif mutants lost the polysialylation activity without changing the proper enzyme expression levels or localization in the Golgi. In addition, we evaluated the enzyme stability of the P motif mutants using newly established calculations of mutation energy, demonstrating that the subtle change of the conformational energy regulates the activity. In the AlphaFold2 model, we found that the P motif was a buried β-strand underneath the known surface motifs unique to ST8SIA2 and ST8SIA4. Taken together, the P motif is a novel buried β-strand that regulates the full activity of polysialyltransferases from the inside of the molecule.

Nicotinic acetylcholine receptors (nAChRs) are drug targets for neurological diseases and disorders, but selective targeting of the large number of nAChR subtypes is challenging. Marine cone snail α-conotoxins are potent blockers of nAChRs and some have been engineered to achieve subtype selectivity. This engineering effort would benefit from rapid computational methods able to predict mutational energies, but current approaches typically require high-resolution experimental structures, which are not widely available for α-conotoxin complexes. Herein, five mutational energy prediction methods were benchmarked using crystallographic and mutational data on two acetylcholine binding protein/α-conotoxin systems. Molecular models were developed for six nAChR subtypes in complex with five α-conotoxins that were studied through 150 substitutions. The best method was a combination of FoldX and molecular dynamics simulations, resulting in a predictive Matthews Correlation Coefficient (MCC) of 0.68 (85 % accuracy). Novel α-conotoxin mutants designed using this method were successfully validated by experimental assay with improved pharmaceutical properties. This work paves the way for the rapid design of subtype-specific nAChR ligands and potentially accelerated drug development.

The study investigates the effect of different parameters for the removal of SO2 and NOx through a wet process concurrently. Two separate processes have been compared for the removal of flue gases, that is, Ozone/UV and H2O2/UV. The research aims to develop a comparative study for the removal of SO2 and NOx simultaneously using Ozone/H2O2 and UV light and find the energy consumption (also known as EEO) for each process. Combining UV with O3 and H2O2 play a crucial role in generating hydroxyl radicals. Different combinations of Ozone/H2O2, flue gas, and UV intensity were studied at different pH and temperatures of the solution to achieve maximum removal of the flue gases. For, the ozonation process it was observed that the removal% of flue gases increases with increasing UV intensity, and at higher UV intensity (250 W), the removal% for NOx is 92% and SO2 is 95% simultaneously at optimum temperature 308 K. For H2O2/UV process (250 W UV intensity), removal% for NOx is 95% and SO2 is 100% at 313 K, 0.3 LPM flow rate of flue gases. The EEO values obtained for both processes were less than 1 for 95% NOx/SO2 removal efficiency.

In protein design, the energy associated with a huge number of sequence-conformer perturbations has to be routinely estimated. Hence, enhancing the throughput and accuracy of these energy calculations can profoundly improve design success rates and enable tackling more complex design problems. In this work, we explore the possibility of tensorizing the energy calculations and apply them in a protein design framework. We use this framework to design enhanced proteins with anti-cancer and radio-tracing functions. Particularly, we designed multispecific binders against ligands of the epidermal growth factor receptor (EGFR), where the tested design could inhibit EGFR activity in vitro and in vivo. We also used this method to design high-affinity Cu2+ binders that were stable in serum and could be readily loaded with copper-64 radionuclide. The resulting molecules show superior functional properties for their respective applications and demonstrate the generalizable potential of the described protein design approach.

Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.

Molecular dynamics (MD) simulations are now able to routinely reach timescales of microseconds and beyond. This has led to a corresponding increase in the amount of MD trajectory data that needs to be stored, particularly when those trajectories contain explicit solvent molecules. As such, it is desirable to be able to compress trajectory data while still retaining as much of the original information as possible. In this work, we describe compressing MD trajectory data using the NetCDF4/HDF5 file format, making use of quantization of the original positions to achieve better compression ratios. We also analyze the affect this has on both the resulting positions and the energies calculated from post-processing these trajectories, and recommend an optimal level of quantization. Overall we find the NetCDF4/HDF5 format to be an excellent choice for storing MD trajectory data in terms of speed, compressibility, and versatility.

A predictive model for intramolecular hydrogen bond energy (EHB) calculation of polyaromatic ortho-hydroxyaldehydes based on a set of small, functionalized hydrocarbons is developed. The complete data set of 18 compounds was used for this study. The model is based on one of four optional categories of molecular descriptors: geometric, spectroscopic, bond order and topological indices. The model of Wiberg bond indices (WBIs) as descriptors of the CC involved bond based on stepwise regression has acceptable prediction abilities for 14 structures of ortho-hydroxyformylobenzo[a]pyrene derivatives already at the semiempirical level. The presented correlation enables a significantly more rapid and quantitative description of the hydrogen bonding strength than the much more time-consuming MTA method. Thus, WBIs are shown to provide a reliable means for fast prescreening of the energy of chelate hydrogen bonds potentially for any polyaromatic derivatives.

The cold-adapted alpha-amylase (PHA) from Pseudoalteromonas haloplanktis is a psychrophilic enzyme which demonstrates high activity at low temperatures, but poor thermostability. Most of the method only employed the crystal structure to design the target protein. However, the trajectory of protein molecular dynamics (MD) simulation contained clues about the protein stability. In this study, we combined MD simulation and energy optimization methods to design mutations located at non-conserved residues. Two single point mutants (S255K, S340P) and one integrated mutant (S255K/S340P) enhanced thermostability without affecting the optimal catalytic activity. After incubation at 40 °C for 80 min, the residual activities of mutants S255K, S340P and S255K/S340P were 1.6-, 2.4-, and 2.6-fold greater than that of the wild type (WT). Additionally, the catalytic efficiency values (kcat/Km) of S255K, S340P, and S255K/S340P also increased 1.9-, 2.0-, and 2.7-fold when compared to WT.

BACKGROUND: With advances in proteomics, it is essential to investigate the molecularlevel participation of IL27 and gp130 to hinder HIV infection. Their interaction causes cell-cycle arrest in HIV+ cells by activating the receptor-associated JAK signaling and causing apoptosis of cancerous cells.
METHODS: The best human wild-type WT_IL27 model was prepared after varied molecular modeling techniques. The vital tyrosine residues in WT_IL27 were identified, mutated and IL27 was re-modeled. Both wild-type and mutant IL27 were docked individually with human gp130 ectodomain complex. Best cluster sized complex was opted and the complexes (WT and MT) were MD (molecular dynamics) simulated. Protein-protein interacting residues, binding patterns, thermodynamic stability, solvent accessibility and many such parameters were evaluated to affirm the stability in the mutant complex. Statistical significances were drawn too.
CONCLUSIONS: With statistical significances also, the mutant type (MT) IL27 was comprehended as the most stable one. Their functionality remained the same. Ionic interactions were the most dominating ones. Exceptionally several Arg residues from MT_IL27 appeared to play a major role, thereby stabilizing the simulated MT_IL27-gp130 complexes. Manifold energy estimations for the complexes, electrostatic potential and increment in %helices and %β-sheets revealed the simulated MT_IL27-gp130 complex to be more stable. In the MT_complex, residues forming 3-ten helices remained constant with major increase in α-helices. This thereby infers the complex as the steadiest and most interactive one.
CONCLUSIONS: The residual exploration with the detailed structural analysis would aid in the effective drug discovery by targeting the drugs at the interacting sites with the specific binding patterns as analyzed from the study. Conformational stability and other several parameters for thermodynamic stability and accomplishment of strong interaction were also explored. Altogether, this probe provides a limelight towards the mutational alterations in WT IL27, which might allow it to act as a strong peptide inhibitor by shielding HIV entry, more potently.