Journal of Theoretical and Computational Chemistry

Computational chemistry offers variety of tools to study properties of biological macromolecules. These tools vary in terms of levels of details from quantum mechanical treatment to numerous macroscopic approaches. Here, we provide a review of computational chemistry algorithms and tools for modeling the effects of genetic variations and their association with diseases. Particular emphasis is given on modeling the effects of missense mutations on stability, conformational dynamics, binding, hydrogen bond network, salt bridges, and pH-dependent properties of the corresponding macromolecules. It is outlined that the disease may be caused by alteration of one or several of above-mentioned biophysical characteristics, and a successful prediction of pathogenicity requires detailed analysis of how the alterations affect the function of involved macromolecules. The review provides a short list of most commonly used algorithms to predict the molecular effects of mutations as well.

Rett Syndrome is a rare genetic disorder exclusively seen in girls. Approximately 95% of RTT cases is caused by mutations in the MeCP2 gene which codes for Methyl-CpG-binding protein 2 (MeCP2). In this review, first, a brief introductory review of Rett Syndrome, MeCP2 protein structure and function, mutation types and frequencies, and phenotype–genotype relationships were provided. After that, the current knowledge on the wild-type and mutant MeCP2 protein structure and dynamics as well as its binding to DNA is reviewed. The review particularly focuses on computational (such as molecular dynamics) and experimental (such as electrophoretic mobility shift assays) studies on the MeCP2 binding to different types of DNA as well as the computational and experimental (such as circular dichroism) studies on the stability changes upon mutations. In the end, a brief opinion on future outlook for further computational studies is provided.

Background: The multiple endocrine neoplasia type 1 (MEN1) gene located on chromosome 11q13 encodes menin protein. Previously reported mutations were thought to result in loss of function of menin protein and that they are associated with multiple endocrine neoplasia 1 disorder. However, recently menin has also been characterized as an oncosuppressor protein and it was suggested that mutations in it are associated with various other tumors. Studies indicate that the menin protein stimulates the estrogen receptor (ER) that in turn increases the predisposition for inherited breast cancer.

Methods: Here, we used our supervised in-house combinatory in-silico predictor method to investigate the impact of unclassified missense mutations in MEN1 gene found in breast cancer tissue. We also examined the biophysical and biochemical properties to predict the effects of these missense variants on the menin protein stability and interactions. The results are compared with the effects of known pathogenic mutations in menin causing neoplasia.

Results: Our analysis indicates that some of the variants found in breast cancer tissue show similar pattern of destabilizing the menin protein and its interactions as the pathogenic variants associated with neoplasia. Taking together with the results of our in-silico consensus predictor, we classify missense mutations in menin protein found in breast cancer tissue into pathogenic and benign, and thus, suggesting as an indicator for early detection of elevated breast cancer risk.

Hereditary spastic paraplegias (HSPs) are a genetically heterogeneous collection of neurodegenerative disorders categorized by progressive lower-limb spasticity and frailty. The complex HSP forms are characterized by various neurological features including progressive spastic weakness, urinary sphincter dysfunction, extra pyramidal signs and intellectual disability (ID). The kinesin superfamily proteins (KIFs) are microtubule-dependent molecular motors involved in intracellular transport. Kinesins directionally transport membrane vesicles, protein complexes, and mRNAs along neurites, thus playing important roles in neuronal development and function. Recent genetic studies have identified kinesin mutations in patients with HSPs. In this study, we used the computational approaches to investigate the 40 missense mutations associated with HSP and ID in KIF1A and KIF5A. We performed homology modeling to construct the structures of kinesin–microtubule binding domain and kinesin–tubulin complex. We applied structure-based energy calculation methods to determine the effects of missense mutations on protein stability and protein–protein interaction. The results revealed that the most of disease-causing mutations could change the folding free energy of kinesin motor domain and the binding free energy of kinesin–tubulin complex. We found that E253K associated with ID in KIF1A decrease the protein stability of kinesin motor domains. We showed that the HSP mutations located in kinesin–tubulin complex interface, such as K253N and R280C in KIF5A, can destabilize the kinesin–tubulin complex. The computational analysis provides useful information for understanding the roles of kinesin mutations in the development of ID and HSPs.

The seeds of Daucus carota, traditionally used by women in many countries to prevent conception, were proved to have negative impact on reproductive hormone levels as well as on the estrous cycles in albino mice affecting the fertility status. This study is an attempt to investigate the possible role of polyphenols present in the seeds in hampering the reproductive processes. The Molecular Docking, Molecular Dynamics (MD) simulation and binding free energy calculation studies reveal that six polyphenols present in the seeds can bind with the active sites of human Estrogen Receptor (ER) and may interfere in the estrogen signaling in human. These polyphenols were found to bind to a conservative pocket of ER$\alpha$, which is comprised of residues 343–388, 421–428 and 525–540. Docking studies indicated the presence of strong hydrogen bonding, pi–pi interactions and numerous hydrophobic interactions that stabilize the ER$\alpha$-polyphenol complexes.

The docked complexes were further subjected to MM/GBSA analysis to calculate binding free energies. Molecular dynamic simulation studies carried out for a period of 20ns revealed low RMS deviation values suggesting high accuracy of the docking poses and stability of the complexes. Out of the six polyphenols, catechin and epicatechin have shown highest binding affinity towards the ER$\alpha$ receptor. These findings will help in identifying ER modulators of plant origin targeting ER alpha and predicting their effects on the reproductive hormone homeostasis. Moreover, this study may form preliminary basis for further identification of potential herbal antifertility agents.

Density functional theory (DFT) calculations are performed to study the conformational flexibility of secondary structures in amyloid beta (A$\beta$) polypeptide. In DFT, M06-2X/6-31$+$G(d, p) method is used to optimize the secondary structures of 2LFM and 2BEG in gas phase and in solution phase. Our calculations show that the secondary structures are energetically more stable in solution phase than in gas phase. This is due to the presence of strong solvent interaction with the secondary structures considered in this study. Among the backbone $\phi$ and $\psi$ dihedral angles, $\psi$ varies significantly in sheet structure. This is due to the absence of intermolecular hydrogen bond (H-bond) interactions in sheets considered in this study. Our calculations show that the conformational transition of helix/coil to sheet or vice-versa is due to the floppiness of the amino acid residues. This is observed from the Ramachandran map of the studied secondary structures. Further, it is noted that the intramolecular H-bond interactions play a significant role in the conformational transition of secondary structures of A$\beta$.

Momordica charantia, known as “São Caetano Melon”, is a medicinal plant popularly used for its antitumor, anticarcinogenic, hypoglycemic, and other properties. Studies in mice have demonstrated its activity in vivo against breast, prostate, and ovarian cancers. In vitro studies have also indicated potential efficacy against cervical solid tumors, and breast cancer. In these studies, we sought to evaluate these putative activities. Our methods included use of in silico tools to assess predicted biological activities, pharmacodynamics, and toxicity. We also performed docking and pharmacophore modeling studies. We found phenolic compounds, flavonoids, alkaloids, and triterpenes. Four flavonoids possess predicted anticarcinogenic activity, and affinity for estrogen receptors. Quercetin was selected for the study because it is the most prevalent representative of its class.