Role of non-covalent interactions for determining the folding rate of two-state proteins

Induction of zinc conjugated with Doxorubicin for the prevention of aggregating β-catenin in the Wnt signaling pathway investigated through computational approaches

Canonical Wnt signaling plays a key role in tumor cell proliferation which correlates with the accumulation of β-catenin resulting inactivation of the network of targets such as GSK3β, Axin, CK1. Uncontrolled expression of β-catenin leads to different types of cancers and other diseases such as sarcoma and mesenchymal tumor formation. However, β-catenin is an attractive target for cervical cancer. In the present study, the compounds such as Doxorubicin and Zinc conjugated with Doxorubicin were screened against β-catenin using Molecular Docking, Molecular Dynamics Simulation, MM/GBSA, and DFT approaches to explore their insights. The study further demonstrated that the binding energy of Zn conjugated with Doxorubicin has shown -7.2 kcal/mol and Doxorubicin registers -5.9 kcal/mol against β-catenin. The disruption between the β-catenin/Tcf-4 complex was observed through the Zinc-Doxorubicin complex, both the proteins are separated about 12 Å. The Zn-Doxorubicin was stabilized with the hydrophobic residues such as Val349 of β-catenin and Phe21 of Tcf-4. The DFT analysis using the B3LYP/6-31g(d,p) method explores that Zn-doxorubicin in complex with the binding site residues has shown the HOMO-LUMO gap of 2.55 eV. The binding free energy calculations exhibit the Zn conjugated Doxorubicin favors in the study by showing ~ 3 kcal/mol difference with Doxorubicin. The Zn-conjugated Doxorubicin will be discussed in the context of cervical cancer with the hope of improving drug efficacy and reducing toxicities for the betterment of the patient's quality of life.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Benchmarking Swaths of Intermolecular Interaction Components with Symmetry-Adapted Perturbation Theory

A benchmark database for interaction energy components of various noncovalent interactions (NCIs) along their dissociation curve is one of the essential needs in theoretical chemistry, especially for the development of force fields and machine-learning methods. We utilize DFT-SAPT or SAPT(DFT) as one of the most accurate methods to generate an extensive stock of the energy components, including dispersion energies extrapolated to the complete basis set limit (CBS). Precise analyses of the created data, and benchmarking the total interaction energies against the best available CCSD(T)/CBS values, reveal different aspects of the methodology and the nature of NCIs. For example, error cancellation effects between the S2 approximation and nonexact xc-potentials occur, and large charge transfer energies in some systems, including heavy atoms, can explain the lower accuracy of DFT-SAPT. This method is perfect for neutral complexes containing light nonmetals, while other systems with heavier atoms should be treated carefully. In the last part, a representative data set for all NCIs is extracted from the original data.

Inclusion of More Physics Leads to Less Data: Learning the Interaction Energy as a Function of Electron Deformation Density with Limited Training Data

Machine learning (ML) approaches to predicting quantum mechanical (QM) properties have made great strides toward achieving the computational chemist's holy grail of structure-based property prediction. In contrast to direct ML methods, which encode a molecule with only structural information, in this work, we show that QM descriptors improve ML predictions of dimer interaction energy, both in terms of accuracy and data efficiency, by incorporating electronic information into the descriptor. We present the electron deformation density interaction energy machine learning (EDDIE-ML) model, which predicts the interaction energy as a function of Hartree-Fock electron deformation density. We compare its performance with leading direct ML schemes and modern DFT methods for the prediction of interaction energies for dimers of varying charge type, size, and intermolecular separation. Under a low-data regime, EDDIE-ML outperforms other direct ML schemes and is the only model readily transferrable to larger, more complex systems including base pair trimers and porous cages. The underlying physical connection between the density and interaction energy enables EDDIE-ML to reach an accuracy comparable to modern DFT functionals in fewer training data points compared to other ML methods.

Competitive and cooperative n →π* and n →σ* interactions in benzaldehyde-formaldehyde: rotational characterization

The rotational spectrum of the 1 : 1 benzaldehyde-formaldehyde complex has been investigated by pulsed jet Fourier transform microwave spectroscopy combined with ab initio calculations. The two most stable isomers were observed, with the relative abundance ratio N_I/N_II≈ 3/1 estimated with intensity measurements. Both observed isomers are stabilized by one dominating O[double bond, length as m-dash]CO tetrel bond (n →π* interaction) and one secondary C-HO hydrogen bond. Natural bond orbital analysis and electron localization function analysis were applied to characterize the nature of the noncovalent interactions in the target complex.

Computational Analysis of Non-covalent Interactions in Phycocyanin Subunit Interfaces

Protein-protein interactions are an important phenomenon in biological processes and functions. We used the manually curated non-redundant dataset of 118 phycocyanin interfaces to gain additional insight into this phenomenon using a robust inter-atomic non-covalent interaction analyzing tool PPCheck. Our observations indicate that there is a relatively high composition of hydrophobic residues at the interfaces. Most of the interface residues are clustered at the middle of the range which we call "standard-size" interfaces. Furthermore, the multiple interaction patterns founded in the present study indicate that more than half of the residues involved in these interactions participate in multiple and water-bridged hydrogen bonds. Thus, hydrogen bonds contribute maximally towards the stability of protein-protein complexes. The analysis shows that hydrogen bond energies contribute to about 88 % to the total energy and it also increases with interface size. Van der Waals (vdW) energy contributes to 9.3 %±1.7 % on average in these complexes. Moreover, there is about 1.9 %±1.5 % contribution by electrostatic energy. Nevertheless, the role by vdW and electrostatic energy could not be ignored in interface binding. Results show that the total binding energy is more for large phycocyanin interfaces. The normalized energy per residue was less than -16 kJ mol^-1 , while most of them have energy in the range from -6 to -14 kJ mol^-1 . The non-covalent interacting residues in these proteins were found to be highly conserved. Obtained results might contribute to the understanding of structural stability of this class of evolutionary essential proteins with increased practical application and future designs of novel protein-bioactive compound interactions.

Sensitivity of core-level spectroscopy to electrostatic environments of nitrile groups: An ab initio study

Ab initio quantum chemistry calculations have been performed to probe the influence of hydrogen bonding on the electronic structure of hydrogen cyanide (HCN). Our calculations determine the origin of nitrogen-specific Raman spectral features from resonant inelastic X-ray scattering occurring in the presence of a water molecule and an electric dipole field. The similarity of the two interactions in altering the electronic structure of the nitrogen atom differs only in the covalent contributions from the water molecule. The CN stretching mode as a structural probe was also investigated to study the electronic origin of the anomalous frequency shift of the nitrile group when subjected to hydrogen bonding and an electrostatic dipole field. The major changes in the electronic structure of HCN are electrostatic in nature and originate from dipole-dipole interactions. The relative shifts of the CN stretching frequency are in good agreement with those experimentally observed.

Anion assisted supramolecular self-assemblies of succinate and malate adducts: crystal structures and theoretical modelling

A series of five dicarboxylic acid complexes of succinic acid and malic acid with various substituted amines have been synthesized and structurally analyzed; also the supramolecular organizations within their crystalline solids have been studied.

Direct correlation between proteins' folding rates and their amino acid compositions: An ab initio folding rate prediction

Discovering the mechanism of protein folding, in molecular biology, is a great challenge. A key step to this end is to find factors that correlate with protein folding rates. Over the past few years, many empirical parameters, such as contact order, long-range order, total contact distance, secondary structure contents, have been developed to reflect the correlation between folding rates and protein tertiary or secondary structures. However, the correlation between proteins' folding rates and their amino acid compositions has not been explored. In the present work, we examined systematically the correlation between proteins' folding rates and their amino acid compositions for two-state and multistate folders and found that different amino acids contributed differently to the folding progress. The relation between the amino acids' molecular weight and degeneracy and the folding rates was examined, and the role of hydrophobicity in the protein folding process was also inspected. As a consequence, a new indicator called composition index was derived, which takes no structure factors into account and is merely determined by the amino acid composition of a protein. Such an indicator is found to be highly correlated with the protein's folding rate (r > 0.7). From the results of this work, three points of concluding remarks are evident. (1) Two-state folders and multistate folders have different rate-determining amino acids. (2) The main determining information of a protein's folding rate is largely reflected in its amino acid composition. (3) Composition index may be the best predictor for an ab initio protein folding rate prediction directly from protein sequence from the standpoint of practical application.

A localized specific interaction alters the unfolding pathways of structural homologues

Reductive unfolding studies of proteins are designed to provide information about intramolecular interactions that govern the formation (and stabilization) of the native state and about folding/unfolding pathways. By mutating Tyr92 to G, A, or L in the model protein, bovine pancreatic ribonuclease A, and through analysis of temperature factors and molecular dynamics simulations of the crystal structures of these mutants, it is demonstrated that the markedly different reductive unfolding rates and pathways of ribonuclease A and its structural homologue onconase can be attributed to a single, localized, ring-stacking interaction between Tyr92 and Pro93 in the bovine variant. The fortuitous location of this specific stabilizing interaction in a disulfide-bond-containing loop region of ribonuclease A results in the localized modulation of protein dynamics that, in turn, enhances the susceptibility of the disulfide bond to reduction leading to an alteration in the reductive unfolding behavior of the homologues. These results have important implications for folding studies involving topological determinants to obtain folding/unfolding rates and pathways, for protein structure-function prediction through fold recognition, and for predicting proteolytic cleavage sites.

Relative importance of secondary structure and solvent accessibility to the stability of protein mutants

Understanding the factors influencing the stability of protein mutants is an important task in molecular and computational biology. In this work, we have approached this problem by examining the relative importance of secondary structure and solvent accessibility of the mutant residue for understanding/predicting the stability of protein mutants. We have used hydrophobic, electrostatic and hydrogen bond free energy terms and nine unique physicochemical, energetic and conformational properties of amino acids in the present study and these parameters have been related with changes in thermal stability (DeltaTm) of all the single mutants of lysozymes based on single and multiple correlation coefficients. As expected the properties reflecting hydrophobicity and hydrophobic free energy play a major role to distinguish stabilizing and destabilizing mutants. The hydrophobic free energy due to carbon and nitrogen atoms distinguish the stability of coil and strand mutations to the accuracy of 100 and 90%, respectively. In agreement with previous results, the subgroup classification based on secondary structure and the information about its location in the structure yielded good relationship with the experimental DeltaTm. We revealed that the secondary structure information is equally or more important than solvent accessibility for understanding the stability of protein mutants. The comparison of amino acid properties with free-energy terms indicate that the energetic contribution explains the mutant stability better in coil region whereas the amino acid properties do better in strand region. Further, the combination of free energies with amino acid properties increased the correlation significantly. The present study demonstrates the importance of classifying the mutants based on secondary structure to the stability of proteins upon mutations.