Contribution of proline to the pre-structuring tendency of transient helical secondary structure elements in intrinsically disordered proteins

Background

IDPs function without relying on three-dimensional structures. No clear rationale for such a behavior is available yet. PreSMos are transient secondary structures observed in the target-free IDPs and serve as the target-binding “active” motifs in IDPs. Prolines are frequently found in the flanking regions of PreSMos. Contribution of prolines to the conformational stability of the helical PreSMos in IDPs is investigated.

Methods

MD simulations are performed for several IDP segments containing a helical PreSMo and the flanking prolines. To measure the influence of flanking-prolines on the structural content of a helical PreSMo calculations were done for wild type as well as for mutant segments with Pro → Asp, His, Lys, or Ala. The change in the helicity due to removal of a proline was measured both for the PreSMo region and for the flanking regions.

Results

The α-helical content in ~ 70% of the helical PreSMos at the early stage of simulation decreases due to replacement of an N-terminal flanking proline by other residues whereas the helix content in nearly all PreSMos increases when the same replacements occur at the C-terminal flanking region. The helix destabilizing/terminating role of the C-terminal flanking prolines is more pronounced than the helix promoting effect of the N-terminal flanking prolines.

General significance

This work represents a novel example demonstrating that a proline is encoded in an IDP with a defined purpose. The helical PreSMos presage their target-bound conformations. As they most likely mediate IDP-target binding via conformational selection their helical content can be an important feature for IDP function.     

Evaluation of protein–ligand affinity prediction using steered molecular dynamics simulations

In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein–ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski’s equality was employed compared with the second-order cumulant expansion equation of Jarzynski’s equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes.     

Structural modulation of factor VIIa by full-length tissue factor (TF1-263): implication of novel interactions between EGF2 domain and TF

Tissue factor (TF)-mediated factor VII (FVII) activation and a subsequent proteolytic TF-FVIIa binary complex formation is the key step initiating the coagulation cascade, with implications in various homeostatic and pathologic scenarios. TF binding allosterically modifies zymogen-like free FVIIa to its highly catalytically active form. As a result of unresolved crystal structure of the full-length TF_1-263-FVIIa binary complex and free FVIIa, allosteric alterations in FVIIa following its binding to full-length TF and the consequences of these on function are not entirely clear. The present study aims to map and identify structural alterations in FVIIa and TF resulting from full-length TF binding to FVIIa and the key events responsible for enhanced FVIIa activity in coagulation. We constructed the full-length TF_1-263-FVIIa membrane bound complex using computational modeling and subjected it to molecular dynamics (MD) simulations. MD simulations showed that TF alters the structure of each domain of FVIIa and these combined alterations contribute to enhanced TF-FVIIa activity. Detailed, domain-wise investigation revealed several new non-covalent interactions between TF and FVIIa that were not found in the truncated soluble TF-FVIIa crystal structure. The structural modulation of each FVIIa domain imparted by TF indicated that both inter and intra-domain communication is crucial for allosteric modulation of FVIIa. Our results suggest that these newly formed interactions can provide additional stability to the protease domain and regulate its activity profile by governing catalytic triad (CT) orientation and localization. The unexplored newly formed interactions between EGF2 and TF provides a possible explanation for TF-induced allosteric activation of FVIIa.     

SPR-MS in functional proteomics

The mapping of protein networks and the establishment of thefunctional relationships between expressed proteins and theireffects on cellular processes represents a great challenge forfunctional or interaction proteomics. The combination of surfaceplasmon resonance (SPR)-based technology with mass spectrometry(MS) has created a unique analytical tool for functional proteomicsinvestigations. Proteins are affinity purified, quantified andcharacterised in terms of their interactions, while the massspectrometer identifies and structurally characterises the biomolecules.Recent developments have led to a closer integration of thesekey technologies, providing a combined approach which enablesidentification of proteins selected on the basis of their functionalbinding criteria. In addition to a historical overview of thisfield, some recent detailed examples of combined SPR-MS approacheswill be reviewed in a number of key application areas, includingligand fishing, peptide sequence and post-translational modificationanalysis by SPR-MS/MS and enzyme inhibitor screening.      

CD4-gp120 interaction interface - a gateway for HIV-1 infection in human: molecular network,modeling and docking studies

The major causative agent for Acquired Immune Deficiency Syndrome (AIDS) is Human Immunodeficiency Virus-1 (HIV-1). HIV-1 is a predominant subtype of HIV which counts on human cellular mechanism virtually in every aspect of its life cycle. Binding of viral envelope glycoprotein-gp120 with human cell surface CD4 receptor triggers the early infection stage of HIV-1. This study focuses on the interaction interface between these two proteins that play a crucial role for viral infectivity. The CD4–gp120 interaction interface has been studied through a comprehensive protein–protein interaction network (PPIN) analysis and highlighted as a useful step towards identifying potential therapeutic drug targets against HIV-1 infection. We prioritized gp41, Nef and Tat proteins of HIV-1 as valuable drug targets at early stage of viral infection. Lack of crystal structure has made it difficult to understand the biological implication of these proteins during disease progression. Here, computational protein modeling techniques and molecular dynamics simulations were performed to generate three-dimensional models of these targets. Besides, molecular docking was initiated to determine the desirability of these target proteins for already available HIV-1 specific drugs which indicates the usefulness of these protein structures to identify an effective drug combination therapy against AIDS.     

A note on sequence-dependence of DNA structure

A circular dichroism study was conducted on the solution structure of several different oligonucleotides, whose X-ray structures have been solved. It is suggested that in aqueous solution the oligonucleotides can form structures that maintain geometrical elements which are typical of B-DNA, A-DNA, and their intermediate forms. It is shown that 5'GGATGGGAG:5'CTCCCATCC, which forms an A-DNA helix in the crystal state (McCall et al. 1986), in aqueous solution maintains an A-DNA like structure at temperatures below 10 degrees C. At temperatures between 10 degrees C and 25 degrees C it shows a tendency to form an intermediate structure between A-DNA and B-DNA. Also, it is shown that TFE does not cause a transition from B-DNA to A-DNA helix in short DNA fragments, but instead disrupts the helix.     

Targeted purification development enabled by computational biophysical modeling

Chromatographic and non‐chromatographic purification of biopharmaceuticals depend on the interactions between protein molecules and a solid–liquid interface. These interactions are dominated by the protein–surface properties, which are a function of protein sequence, structure, and dynamics. In addition, protein–surface properties are critical for in vivo recognition and activation, thus, purification strategies should strive to preserve structural integrity and retain desired pharmacological efficacy. Other factors such as surface diffusion, pore diffusion, and film mass transfer can impact chromatographic separation and resin design. The key factors that impact non‐chromatographic separations (e.g., solubility, ligand affinity, charges and hydrophobic clusters, and molecular dynamics) are readily amenable to computational modeling and can enhance the understanding of protein chromatographic. Previously published studies have used computational methods such as quantitative structure–activity relationship (QSAR) or quantitative structure–property relationship (QSPR) to identify and rank order affinity ligands based on their potential to effectively bind and separate a desired biopharmaceutical from host cell protein (HCP) and other impurities. The challenge in the application of such an approach is to discern key yet subtle differences in ligands and proteins that influence biologics purification. Using a relatively small molecular weight protein (insulin), this research overcame limitations of previous modeling efforts by utilizing atomic level detail for the modeling of protein–ligand interactions, effectively leveraging and extending previous research on drug target discovery. These principles were applied to the purification of different commercially available insulin variants. The ability of these computational models to correlate directionally with empirical observation is demonstrated for several insulin systems over a range of purification challenges including resolution of subtle product variants (amino acid misincorporations). Broader application of this methodology in bioprocess development may enhance and speed the development of a robust purification platform. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 31:154–164, 2015