Important amino acid residues involved in folding and binding of protein–protein complexes

Allosteric mechanism of synergistic effect in α- and β-amylase mixtures

Alpha-amylase and beta-amylase coexist as mixtures in industrial production, and the two amylases have active synergistic effects when they approach each other. These effects are due to enhanced enzyme binding affinity for the substrate and the rate of particle hydrolysis. Here, we report the allosteric mechanism of this synergistic effect in α- and β-amylase mixtures. The assay showed higher activity after mixing α- and β-amylase. Molecular docking showed that α- and β-amylase create a stable dual-enzyme complex with high binding energy, and that complex formation does not affect the exposure of respective active sites. β-Amylase is specifically bound to the B domain of α-amylase, and the dynamic plasticity of the B domain makes it move spatially, and this adjustment leads to a more open conformation in the active site of α-amylase. Because the enzymes binding make the complex more stable, the degree to which the relative activity of the dual-enzyme complex is inhibited is significantly reduced. After enzyme hydrolysis, the products maltose and glucose accumulate and produce competitive inhibition, which explains the relative activity decrease of the later-stage dual-enzyme cooperation. Structural characterization by FT-IR and CD spectroscopy did not reveal significant changes in respective secondary structures after enzyme binding.

Machine Learning Uncovers Vascular Endothelial Cell Identity Genes by Expression Regulation Features in Single Cells

Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and many diseases involving cell identity dysregulation. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes are regulated with unique epigenetic signatures, we found cell identity genes exhibit distinctive genetic sequence signatures, e.g., unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, enables SCIG to uncover the identity genes of a cell without a need for comparison to other cells. Cell identity gene score defined by SCIG surpassed expression value in network analysis to uncover master transcription factors regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master transcription factors for cell identity refinement. SCIG is publicly available at https://github.com/kaifuchenlab/SCIG , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.

Expression of Phospholipase D Family Member 6 in Bovine Testes and Its Molecular Characteristics

Spermatogonial stem cells (SSCs) are the only primitive spermatogonial cells in males that can naturally transmit genetic information to their offspring and replicate throughout their lives. Phospholipase D family member 6 (PLD6) has recently been found to be a surface marker for SSCs in mice and boars; however, it has not been validated in cattle. The results of reversed transcription-polymerase chain reaction (RT-PCR) and quantitative real-time PCR (qRT-PCR) found that the relative expression of the PLD6 gene in the testicular tissues of two-year-old Simmental calves was significantly higher than that of six-month-old calves. Immunofluorescent staining further verified the expression of PLD6 protein in bovine spermatogenic cells like germ cell marker DEAD box helicase 4 (DDX4, also known as VASA). Based on multiple bioinformatic databases, PLD6 is a conservative protein which has high homology with mouse Q5SWZ9 protein. It is closely involved in the normal functioning of the reproductive system. Molecular dynamics simulation analyzed the binding of PLD6 as a phospholipase to cardiolipin (CL), and the PLD6-CL complex showed high stability. The protein interaction network analysis showed that there is a significant relationship between PLD6 and piwi-interacting RNA (piRNA) binding protein. PLD6 acts as an endonuclease and participates in piRNA production. In addition, PLD6 in bovine and mouse testes has a similar expression pattern with the spermatogonium-related genes VASA and piwi like RNA-mediated gene silencing 2 (PIWIL2). In conclusion, these analyses imply that PLD6 has a relatively high expression in bovine testes and could be used as a biomarker for spermatogenic cells including SSCs.

Halogen-Based 17β-HSD1 Inhibitors: Insights from DFT, Docking, and Molecular Dynamics Simulation Studies

The high expression of 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1) mRNA has been found in breast cancer tissues and endometriosis. The current research focuses on preparing a range of organic molecules as 17β-HSD1 inhibitors. Among them, the derivatives of hydroxyphenyl naphthol steroidomimetics are reported as one of the potential groups of inhibitors for treating estrogen-dependent disorders. Looking at the recent trends in drug design, many halogen-based drugs have been approved by the FDA in the last few years. Here, we propose sixteen potential hydroxyphenyl naphthol steroidomimetics-based inhibitors through halogen substitution. Our Frontier Molecular Orbitals (FMO) analysis reveals that the halogen atom significantly lowers the Lowest Unoccupied Molecular Orbital (LUMO) level, and iodine shows an excellent capability to reduce the LUMO in particular. Tri-halogen substitution shows more chemical reactivity via a reduced HOMO-LUMO gap. Furthermore, the computed DFT descriptors highlight the structure-property relationship towards their binding ability to the 17β-HSD1 protein. We analyze the nature of different noncovalent interactions between these molecules and the 17β-HSD1 using molecular docking analysis. The halogen-derived molecules showed binding energy ranging from -10.26 to -11.94 kcal/mol. Furthermore, the molecular dynamics (MD) simulations show that the newly proposed compounds provide good stability with 17β-HSD1. The information obtained from this investigation will advance our knowledge of the 17β-HSD1 inhibitors and offer clues to developing new 17β-HSD1 inhibitors for future applications.

Residue-specific binding mechanisms of PD-L1 to its monoclonal antibodies by computational alanine scanning

Programmed cell death 1 receptor (PD-1) on the surface of T cells and its ligand 1 (PD-L1) are immune checkpoint proteins. Treating cancer patients with inhibitors blocking this checkpoint has significantly prolonged the survival rate of patients. In this study, we examined several monoclonal antibodies (mAbs) of PD-L1 and studied their detailed binding mechanism to PD-L1. An efficient computational alanine scanning method was used to perform quantitative analysis of hotspot residues that are important for PD-1/PD-L1 binding. A total of five PD-L1/mAb complexes were investigated and hotspots on both PD-L1 and mAbs were predicted. Our result shows that PD-L1M115 and PD-L1Y123 are two relatively important hotspots in all the five PD-L1/mAb binding complexes. It is also found that the important residues of mAbs binding to PD-L1M115 and PD-L1Y123 are similar to each other. The computational alanine scanning result is compared to the experimental measurements that are available for two of the mAbs (KN035 and atezolizumab). The calculated alanine scanning result is in good agreement with the experimental data with a correlation coefficient of 0.87 for PD-L1/KN035 and 0.6 for PD-L1/atezolizumab. Our computation found more hotspots on PD-L1 in the PD-L1/KN035 complex than those in the PD-L1/atezolizumab system, indicating stronger binding affinity in the former than the latter, which is in good agreement with the experimental finding. The present work provides important insights for the design of new mAbs targeting PD-L1.

Computational investigation of FDA approved drugs as selective PARP-1 inhibitors by targeting BRCT domain for cancer therapy

Poly(ADP-ribose) polymerase-1 is a promising target for the treatment of cancer due to its involvement in base excision repair pathways for repairing DNA single-strand breaks. However, available PARP-1 inhibitors target a highly conserved PARPs catalytic domain, which causes toxicity due to the off-target activity. Therefore, the present study was hypothesized to identify selective inhibitors by targeting specific protein-protein interacting (PPI) PARP-1 BRCT domain. Moreover, PPI hotspot residues (Gly399, Lys400, Leu401, Lys441 & Lys442) and a druggable pocket was detected to screen small molecule inhibitors. Hence, two FDA approved drug molecules (levoleucovorin and balsalazide) were recognized to fit in the druggable pocket. Since they are already under investigation for anti-cancer activity, thus could be further explored in PARP-1 sensitive cancer cells to expand their selectivity and develop as effective anti-cancer agents. Besides, the study also provides detailed structural insight of PARP-1 and XRCC1 complex through their BRCT domains.

Identification and Analysis of Key Residues in Protein-RNA Complexes

Protein-RNA complexes play important roles in various biological processes. The functions of protein-RNA complexes are dictated by their interactions, binding, stability, and affinity. In this work, we have identified the key residues (KRs), which are involved in both stability and binding. We found that 42 percent of considered proteins share common binding and stabilizing residues, whereas these residues are distinct in 58 percent of the proteins. Overall, 5 percent of stabilizing and 3 percent of binding residues serve as key residues. These residues are enriched with the combination of polar, charged, aliphatic, and aromatic residues. Analysis on subclasses of protein-RNA complexes based on protein structural class, function and RNA type showed that regulatory proteins, and complexes with single stranded RNA and rRNA have appreciable number of key residues. Specifically, Arg, Tyr, and Thr are preferred in most of the subclasses of protein-RNA complexes. In addition, residues with similar chemical behavior have different preferences to be KRs, such that Arg, Tyr, Val, and Thr are preferred over Lys, Trp, Ile, and Ser, respectively. Atomic level contacts revealed that charged and polar-nonpolar contacts are dominant in enzymes, polar in structural, and nonpolar in regulatory proteins. On the other hand, polar-nonpolar contacts are enriched in all these classes of protein-RNA complexes. Further, the influence of sequence and structural features such as conservation score, surrounding hydrophobicity, solvent accessibility, secondary structure, and long-range order in key residues are also discussed. We envisage that the present study provides insights to understand the structural and functional aspects of protein-RNA complexes.

A Comprehensive Arabidopsis Yeast Two-Hybrid Library for Protein-Protein Interaction Studies: A Resource to the Plant Research Community

Yeast-two-hybrid (Y2H) cDNA library screening is a valuable tool to uncover protein-protein interactions and represents a widely used method to investigate protein function. However, low transcript representation in cDNA libraries limits the depth of the screening. We have developed a Y2H library with cDNA made from Arabidopsis leaves exposed to several stressors as well as untreated leaves. The library was built using pooled mRNA extracted from plants challenged with plant and human bacterial pathogens, the flg22 elicitor, the phytotoxin coronatine, and several hormones associated with environmental stress responses. The purpose of such a library is to maximize the discovery of protein-protein interactions that occur under optimum conditions as well as during biotic and abiotic stresses.

Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes

Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.