Journal of Theoretical and Computational Chemistry

The protein folding is an important scientific problem and many methods were designed to elucidate the protein folding and obtain insight into the molecular mechanism. A novel means is presented to identify the downhill pathways of protein folding in this paper. This method is based on barrier energy profile projected onto the generalized path length (GPL) with Breadth-first searching (BFS) algorithm. We show the effectiveness of this approach by constructing the barrier energy profile of trpzip2 and comparing with other methods.

p53 is a transcription factor with intrinsically disordered regions that plays an essential role in many cellular processes. As a tumor suppressor, the dysfunction of p53 causes various cancers. p53 can be activated by binding with cofactors in the cell due to stresses or DNA damages. The N-terminal transactivation domain (TAD) of p53 can regulate cell apoptosis by interacting with its binding partners, such as the transcriptional adaptor zinc-binding 2 (Taz2) domain of p300, and cofactors, i.e. cyclic-AMP response element-binding protein (CREB)-binding protein (CBP). The experimentally solved structure of p300 Taz2 and p53 TAD2 has provided insights into the interactions that potentially lead to the structural changes of p53 TAD2 and stabilize the complex. To explore the structural changes as well as the residues that lead to such changes from an isolated state to a bound state in p53 TAD2, we used all-atom molecular dynamics (MD) to simulate two different systems: (1) the p300 Taz2–p53 TAD2 complex and (2) isolated p53 TAD2 (residues 35–59). Although still largely unstructured, residues across the p53 TAD2 contribute significantly to stabilizing the binding between p300 Taz2 and p53 TAD2. Our results suggest that the binding affinity of the p300 Taz2–p53 TAD2 complex originates from hydrophobic and electrostatic interactions. The results are in agreement with previous reports and experimental data. By comparing the two simulated systems, our results not only demonstrate the structural changes of p53 TAD2 after binding with Taz2 but also identify the key residues leading to such changes. We also identify the critical residues that can provide insight into the interaction network between p300 Taz2 and p53 TAD2.

Xanthine Oxidoreductase (XOR) exists in a variety of organisms from bacteria to humans and catalyzes the oxidation of hypoxanthine to xanthine and from xanthine to uric acid. Excessive uric acid could lead to gout and hyperuricemia. In this paper, we have reviewed the recent computational studies on xanthine oxidase inhibition. Computational methods, such as molecular dynamics (molecular mechanics), quantum mechanics, and quantum mechanics/molecular mechanics (QM/MM), have been employed to investigate the binding affinity of xanthine oxidase with synthesized and isolated nature inhibitors. The limitations of different computational methods for xanthine oxidase inhibition studies were also discussed. Implications of the computational approach could be used to help to understand the existing arguments on substrate/product orientation in xanthine oxidase inhibition, which allows designing new inhibitors with higher efficacy.

The molecular big data are highly correlated, and numerous genes are not related. The various classification methods performance mainly rely on the selection of significant genes. Sparse regularized regression (SRR) models using the least absolute shrinkage and selection operator (lasso) and adaptive lasso (alasso) are popularly used for gene selection and classification. Nevertheless, it becomes challenging when the genes are highly correlated. Here, we propose a modified adaptive lasso with weights using the ranking-based feature selection (RFS) methods capable of dealing with the highly correlated gene expression data. Firstly, an RFS methods such as Fisher’s score (FS), Chi-square (CS), and information gain (IG) are employed to ignore the unimportant genes and the top significant genes are chosen through sure independence screening (SIS) criteria. The scores of the ranked genes are normalized and assigned as proposed weights to the alasso method to obtain the most significant genes that were proven to be biologically related to the cancer type and helped in attaining higher classification performance. With the synthetic data and real application of microarray data, we demonstrated that the proposed alasso method with RFS methods is a better approach than the other known methods such as alasso with filtering such as ridge and marginal maximum likelihood estimation (MMLE), lasso and alasso without filtering. The metrics of accuracy, area under the receiver operating characteristics curve (AUROC), and geometric mean (GM-mean) are used for evaluating the performance of the models.

Intrinsically disordered proteins (IDPs) are a class of proteins without stable three-dimensional structures under physiological conditions. IDPs exhibit high dynamic nature and could be described by structural ensembles. As one of the most widely used tools, molecular dynamics (MD) simulation could provide the atomic descriptions of the structural ensemble of IDPs. However, the accuracy of the MD simulation largely depends on the accuracy of the force field. In this paper, we compared the structural ensembles of the activation domain 1 (AD1) in p53 tumor suppressor obtained from the widely used force fields, AMBER99SB-ILDN, CHARMM27, CHARMM36m with different water models. The results show that CHARMM36m generates more extended conformations than other force fields, while CHARMM27 prefers to sample the $\alpha$-helical structure. Moreover, the chemical shifts obtained by CHARMM36m are the closest to the experimental measurements. These results indicate that the CHARMM36m force field performs best in characterizing the structure properties of p53 AD1. Water models are also critical to describe the structural ensemble of IDPs. TIP4P water model can obtain more extended conformations and produce more local helical conformations than the TIP3P model in our simulation. In addition, we also compare the chemical shifts predicted by different chemical shift predicting programs with experimental measurements, the results show that SHIFTX2 obtains the best performance in the chemical shifts prediction.

Receptor–ligand interactions are involved in various biological processes, therefore understanding the binding mechanism and ability to predict the binding mode are essential for many biological investigations. While many computational methods exist to predict the 3D structure of the corresponding complex provided the knowledge of the monomers, here we use the newly developed DelPhiForce steered Molecular Dynamics (DFMD) approach to model the binding of barstar to barnase to demonstrate that first-principles methods are also capable of modeling the binding. Essential component of DFMD approach is enhancing the role of long-range electrostatic interactions to provide guiding force of the monomers toward their correct binding orientation and position. Thus, it is demonstrated that the DFMD can successfully dock barstar to barnase even if the initial positions and orientations of both are completely different from the correct ones. Thus, the electrostatics provides orientational guidance along with pulling force to deliver the ligand in close proximity to the receptor.