Atmospherically Relevant Hydrogen-Bonded Interactions between Methanesulfonic Acid and H2SO4 Clusters: A Computational Study.

Understanding and predicting the energetics of protein-protein interactions is fundamental to the structural modeling of protein complexes. Binding free energy can be approximated as a sum of pairwise atomic or residue contact energies, which are commonly inferred from contact frequencies observed in experimental protein structures. However, such statistically inferred potentials require certain assumptions and approximation. Here, we explore the possibility of deriving atomic and residue contact potentials directly from experimental binding free energy changes following mutation and present a number of such potentials. The first set of potentials is obtained by unweighted least-squares fitting and bootsrap aggregating. The second set is calculated using a weighting scheme optimized against absolute binding affinity data, so as to account for the over-representation of certain complexes, residues, and families of interactions. The congruence of the potentials with known physical chemistry is investigated. The potentials are further validated by ranking and clustering protein-protein docking poses.

An approach using the finite difference solution of the Poisson‐Boltzmann equation to estimate binding free energy changes for two receptor–ligand systems, arabinose binding protein and sulfate binding protein, is presented. The eight calculated binding free energy changes agree with experiment, showing a correlation coefficient of 0.92 and energy deviations of 1 kcal/mol or less. More importantly, the decomposition of solvation and assembly energies in this approach provides an understanding of binding mechanisms and therefore could suggest directions to alter binding affinities. The method is demonstrated to be useful in analyzing experimental binding structures and predicting binding effects of mutants or modified ligands for macromolecular systems, in which the electrostatic forces dominate the overall interaction and the structural perturbations upon modifications are small. © 1995 by John Wiley & Sons, Inc.

AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step toward comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. In this work, we evaluate AF3 complex structures using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations. AF3 complex structures when applied to the most advanced TDL model, MT-TopLap (MultiTask-Topological Laplacian), give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, which is slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.

The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of monoclonal antibodies (mAbs) and vaccines, which are both time-consuming and costly. This underscores the need for efficient computational approaches. Recent advancements, like topological deep learning (TDL), have introduced powerful tools for forecasting emerging dominant variants, yet they require deep mutational scanning (DMS) of viral surface proteins and associated three-dimensional (3D) protein-protein interaction (PPI) complex structures. We propose an AlphaFold 3 (AF3)-assisted multi-task topological Laplacian (MT-TopLap) strategy to address this need. MT-TopLap combines deep learning with topological data analysis (TDA) models, such as persistent Laplacians (PL) to extract detailed topological and geometric characteristics of PPIs, thereby enhancing the prediction of DMS and binding free energy (BFE) changes upon virus mutations. Validation with four experimental DMS datasets of SARS-CoV-2 spike receptor-binding domain (RBD) and the human angiotensin-converting enzyme-2 (ACE2) complexes indicates that our AF3 assisted MT-TopLap strategy maintains robust performance, with only an average 1.1% decrease in Pearson correlation coefficients (PCC) and an average 9.3% increase in root mean square errors (RMSE), compared with the use of experimental structures. Additionally, AF3-assisted MT-TopLap achieved a PCC of 0.81 when tested with a SARS-CoV-2 HK.3 variant DMS dataset, confirming its capability to accurately predict BFE changes and adapt to new experimental data, thereby showcasing its potential for rapid and effective response to fast viral evolution.

The accurate prediction of the binding affinity changes of drugs caused by protein mutations is a major goal in clinical personalized medicine. We have developed an ensemble-based free energy approach called thermodynamic integration with enhanced sampling (TIES), which yields accurate, precise, and reproducible binding affinities. TIES has been shown to perform well for predictions of free energy differences of congeneric ligands to a wide range of target proteins. We have recently introduced variants of TIES, which incorporate the enhanced sampling technique REST2 (replica exchange with solute tempering) and the free energy estimator MBAR (Bennett acceptance ratio). Here we further extend the TIES methodology to study relative binding affinities caused by protein mutations when bound to a ligand, a variant which we call TIES-PM. We apply TIES-PM to fibroblast growth factor receptor 3 (FGFR3) to investigate binding free energy changes upon protein mutations. The results show that TIES-PM with REST2 successfully captures a large conformational change and generates correct free energy differences caused by a gatekeeper mutation located in the binding pocket. Simulations without REST2 fail to overcome the energy barrier between the conformations, and hence the results are highly sensitive to the initial structures. We also discuss situations where REST2 does not improve the accuracy of predictions.

Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve protein-DNA binding, accurately predicting changes in binding free energy (ΔΔG) is valuable for distinguishing pathogenic mutations from benign ones. This study describes the development and optimization of the SAMPDI-3Dv2 machine learning method, which is trained on an expanded database of experimentally measured ΔΔGs. This enhanced model incorporates new features, including the 3D structure of the mutant protein, features of the mutant structure, and a position-specific scoring matrix (PSSM). Benchmarking was conducted using 5-fold cross-validation. The updated SAMPDI-3D model (SAMPDI-3Dv2) achieved Pearson correlation coefficients (PCCs) of 0.68 for protein and 0.80 for DNA mutations. These results represent significant improvements over existing tools. Additionally, the method's rapid execution time enables genome-scale predictions. The improved SAMPDI-3Dv2 shows enhanced predictive performance for analyzing mutations in protein-DNA complexes. By leveraging structural information and an expanded training dataset, SAMPDI-3Dv2 provides researchers with a more accurate and efficient tool for mutation analysis, contributing to identifying pathogenic variants and improving our understanding of cellular function.

The energetic contributions of individual DNA-contacting side chains to specific DNA recognition in the human papillomavirus 16 E2C-DNA complex is small (less than 1.0 kcal mol(-1)), independent of the physical and chemical nature of the interaction, and is strictly additive. The sum of the individual contributions differs 1.0 kcal mol(-1) from the binding energy of the wild-type protein. This difference corresponds to the contribution from the deformability of the DNA, known as "indirect readout." Thus, we can dissect the energetic contribution to DNA binding into 90% direct and 10% indirect readout components. The lack of high energy interactions indicates the absence of "hot spots," such as those found in protein-protein interfaces. These results are compatible with a highly dynamic and "wet" protein-DNA interface, yet highly specific and tight, where individual interactions are constantly being formed and broken.

The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method has been successfully utilized for efficient linear-scaling quantum mechanical (QM) calculation of protein energies. In this work, we applied the EE-GMFCC method for calculation of binding affinity of Endonuclease colicin-immunity protein complex. The binding free energy changes between the wild-type and mutants of the complex calculated by EE-GMFCC are in good agreement with experimental results. The correlation coefficient (R) between the predicted binding energy changes and experimental values is 0.906 at the B3LYP/6-31G*-D level, based on the snapshot whose binding affinity is closest to the average result from the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation. The inclusion of the QM effects is important for accurate prediction of protein-protein binding affinities. Moreover, the self-consistent calculation of PB solvation energy is required for accurate calculations of protein-protein binding free energies. This study demonstrates that the EE-GMFCC method is capable of providing reliable prediction of relative binding affinities for protein-protein complexes. © 2018 Wiley Periodicals, Inc.

Exploring the medicinal significance of bioactive compounds through computational methods is an increasingly practiced approach in contemporary medicinal research. This study aims to assess the antidiabetic potential of compounds extracted from the plant Nyctanthes arbor tristis by evaluating their ability to inhibit the carbohydrate metabolic enzyme α-glucosidase. The research work was conducted through molecular docking calculation, molecular dynamics simulation (MDS), and ADMET prediction techniques. Among the compounds, arbortistoside-C (NAS03), and arbortristoside-D (NAS04) found in the seed of the plant were identified as hit inhibitors of the target protein with docking scores, -9.9 and -9.4 kcal/mol, respectively. The compounds showed a comparable docking score with the drug of diabetes acarbose (-8.6 kcal/mol). Geometrical parameters like radius of gyration, solvent accessibility surface, root mean square deviation, and root mean square fluctuation from MDS supported the stability of the protein-ligand complex. MMPBSA calculations demonstrated the stability and feasibility of the complex with binding free energy changes of -29.06±6.06 and -23.58±8.80 kcal/mol for compounds NAS03 and NAS04, respectively. The ADMET prediction suggested the drug-likeness of the compounds compared with that of the standard drugs. The results could be used in proposing the antidiabetic potential of the two compounds from the plant as a potential inhibitors of α-glucosidase enzyme. Further, in vitro and in vivo experiments on such compounds could be a more reliable path to validate the output of this computational research.

The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Relative binding free energy (RBFE, also referred to as ΔΔG) methods allow the estimation of binding free energy changes after small changes to a ligand scaffold. Here, we propose and evaluate a Siamese convolutional neural network (CNN) for the prediction of RBFE between two bound ligands. We show that our multitask loss is able to improve on a previous state-of-the-art Siamese network for RBFE prediction via increased regularization of the latent space. The Siamese network architecture is well suited to the prediction of RBFE in comparison to a standard CNN trained on the same data (Pearson's R of 0.553 and 0.5, respectively). When evaluated on a left-out protein family, our Siamese CNN shows variability in its RBFE predictive performance depending on the protein family being evaluated (Pearson's R ranging from -0.44 to 0.97). RBFE prediction performance can be improved during generalization by injecting only a few examples (few-shot learning) from the evaluation data set during model training.

Family 19 chitinase from Aeromonas sp. No.10S-24 (72.6 kDa) is composed of two chitin-binding domains (ChBDs), two proline- and threonine-rich (PT-rich) linkers, and a catalytic domain. The purified enzyme was labile in a standard buffer condition and spontaneously degraded into a 46-kDa fragment upon storage at 4 degrees C. The N-terminal sequence of the 46-kDa fragment was found to correspond to the sequence of the C-terminal region of the second PT-rich linker, indicating that the 46-kDa fragment is produced by truncation of the two ChBDs and the two PT-rich linkers from the mature protein, and consists only of the catalytic domain. The hydrolytic activities toward insoluble and soluble substrates were significantly reduced by the truncation of two ChBDs. In addition, antifungal activity determined from the digestion rate of haustoria of powdery mildew was reduced by the ChBD truncation. Although the profile of the time-course of N-acetylglucosamine hexasaccharide [(GlcNAc)6] degradation catalyzed by the ChBD-truncated enzyme was similar to that of the mature enzyme protein, the specific activity of the ChBD-truncated enzyme determined from the rate of hexasaccharide degradation was lower than that of the mature enzyme. The two CBDs appear to be responsible for facilitating the hydrolytic reaction. The sugar residue affinities (binding free energy changes) at the individual subsites, (-2) (-1) (+1) (+2) (+3) (+4), were estimated by modeling the hexasaccharide hydrolysis by the mature and ChBD-truncated enzymes. The truncation of ChBDs was found to strongly affect the affinity at the (-1) site. This situation seems to result in the lower enzymatic activity of the ChBD-truncated enzyme toward the chitinous substrates.

A facile synthesis of chiral polyazamacrocycles and the UV spectroscopic and CD spectra studies on metal complexes

Discovery of highly potent phosphodiesterase-1 inhibitors by a combined-structure free energy perturbation approach

The efficient and reliable computation of relative free energy changes for alchemical mutation is very important in biomolecular simulations. In this study we examine the utility of computing alchemical free energy changes using overlap perturbation techniques. These techniques include both the overlap histogram method and the overlap sampling method (a fully optimized version of the overlap sampling technique is equivalent to Bennett's method), and commonly combine sets of perturbations from both alchemical states of interest to improve a free energy calculation. In addition, we propose new variants, present generalized formulas for overlap perturbation techniques and demonstrate differences from the prevailing direct averaging approach used in alchemical free energy calculations. These overlap perturbation methods can be applied to either equilibrium or non-equilibrium free energy simulations. The alchemical transformation of an adenosine molecule in aqueous solution is used as an example for evaluating these techniques. The calculations show that the methods are capable of producing excellent free energy estimates for an extended range of perturbation magnitudes. We suggest that these overlap perturbation methods should be more widely used for the computation of alchemical free energy changes in biomolecular simulations. † woolf@groucho.med.jhmi.edu

Mutations in ClC channel proteins may cause serious functional changes and even diseases. The function of ClC proteins mainly manifests as Cl(-) transport, which is related to the binding free energies of chloride ions. Therefore, the influence of a mutation on ClC function can be studied by investigating the mutational effect on the binding free energies of chloride ions. The present study provides quantitative and systematic investigations on the influences of residue mutations on the electrostatic binding free energies in Escherichia coli ClC (EcClC) proteins, using all-atom molecular dynamics simulations. It was found that the change of the electrostatic binding free energy decreases linearly with the increase of the residue-chloride ion distance for a mutation. This work reveals how changes in the charge of a mutated residue and in the distance between the mutated residue and the binding site govern the variations in the electrostatic binding free energies and therefore influence the transport of chloride ions and conduction in EcClC. This work would facilitate our understanding of the mutational effects on transport of chloride ions and functions of ClC proteins and provide a guideline to estimate which residue mutations will have great influences on ClC functions.