Illustration of the conformational changes occurring in the proteolytic region of the prodomain, solvent-accessible surface area and multiple structure alignments.

<p>(A) Superimposition of the prodomain proteolytic region (top panel) shows significant conformational changes (R51-A70) in the glycosylated in contrast to non-glycosylated proMMP-9. The right top panel represents times-series of the distance between the Cα atoms of E59-R106 in the glycosylated (black) and non-glycosylated (red) proMMP-9, demonstrating the mobility of the loop. The lower left panel represents the glycosylated proMMP-9 prodomain in closed conformation (first cleavage region protects the second one and the distance between them is 10.9 Å). Lower right panel represents the non-glycosylated proMMP-9 prodomain in open conformation (both cleavages regions are exposed and the distance between them is 24.6 Å). (B) Left and right panel represents the solvent-accessible surface area (SASA in Ų) of two proteolytic fragments for glycosylated and non-glycosylated form of proMMP-9, respectively. (C) Superimposition of seven snapshots, each at 80ns intervals out of the 500ns MD trajectory. Glycans are represented according to the Consortium For Glycomics nomenclature.</p

Role of N-glycosylation in activation of proMMP-9. A molecular dynamics simulations study - Fig 3

<p>(A) Community analysis. Correlated motion of the sub-domains in the glycosylated and non-glycosylated forms of proMMP-9. Sub-domains that are moving together are represented in the same color. (B) Left and right panel represents the comparison of RMSDs of the pro and catalytic domains of glycosylated proMMP-9 and Gal-8N domain and the solvent-accessible surface area (SASA in Ų) of two proteolytic fragments for the complex composed of Gal-8N and glycosylated proMMP-9, respectively. (C) Structural model of the complex composed of Gal-8N and proMMP-9.</p

Hydrogen bond analysis.

<p>Hydrogen bond analysis.</p

Assessment of Amino Acid Electrostatic Parametrizations of the Polarizable Gaussian Multipole Model

Accurate parametrization of amino acids is pivotal for the development of reliable force fields for molecular modeling of biomolecules such as proteins. This study aims to assess amino acid electrostatic parametrizations with the polarizable Gaussian Multipole (pGM) model by evaluating the performance of the pGM-perm (with atomic permanent dipoles) and pGM-ind (without atomic permanent dipoles) variants compared to the traditional RESP model. The 100-conf-combterm fitting strategy on tetrapeptides was adopted, in which (1) all peptide bond atoms (−CO–NH−) share identical set of parameters and (2) the total charges of the two terminal N-acetyl (ACE) and N-methylamide (NME) groups were set to neutral. The accuracy and transferability of electrostatic parameters across peptides with varying lengths and real-world examples were examined. The results demonstrate the enhanced performance of the pGM-perm model in accurately representing the electrostatic properties of amino acids. This insight underscores the potential of the pGM-perm model and the 100-conf-combterm strategy for the future development of the pGM force field

Caspase Cleavage Sites in the Human Proteome: CaspDB, a Database of Predicted Substrates

<div><p>Caspases are enzymes belonging to a conserved family of <b><u>c</u>ysteine-dependent <u>asp</u>artic-specific prote<u>ases</u></b> that are involved in vital cellular processes and play a prominent role in apoptosis and inflammation. Determining all relevant protein substrates of caspases remains a challenging task. Over 1500 caspase substrates have been discovered in the human proteome according to published data and new substrates are discovered on a daily basis. To aid the discovery process we developed a caspase cleavage prediction method using the recently published curated MerCASBA database of experimentally determined caspase substrates and a Random Forest classification method. On both internal and external test sets, the ranking of predicted cleavage positions is superior to all previously developed prediction methods. The <i>in silico</i> predicted caspase cleavage positions in human proteins are available from a relational database: CaspDB. Our database provides information about potential cleavage sites in a verified set of all human proteins collected in Uniprot and their orthologs, allowing for tracing of cleavage motif conservation. It also provides information about the positions of disease-annotated single nucleotide polymorphisms, and posttranslational modifications that may modulate the caspase cleaving efficiency.</p></div

Comparision of CaspDB and Cascleave 2.0 scores.

<p>(A) Probability score comparison of caspase-1 cleavage sites. (B) Caspase-8 cleavage sites. CaspDB and Cascleave 2.0 scores are marked in red and black, respectively.</p

Resolving the Ligand-Binding Specificity in c‑MYC G‑Quadruplex DNA: Absolute Binding Free Energy Calculations and SPR Experiment

We report the absolute binding free energy calculation and surface plasmon resonance (SPR) experiment for ligand binding with the c-MYC G-quadruplex DNA. The unimolecular parallel DNA G-quadruplex formed in nuclease hypersensitivity element III₁ of the c-MYC gene promoter regulates the c-MYC transcription and is recognized as an emerging drug target for cancer therapy. Quindoline derivatives have been shown to stabilize the G-quadruplex and inhibit the c-MYC expression in cancer cells. NMR revealed two binding sites located at the 5′ and 3′ termini of the G-quadruplex. Questions about which site is more favored and the basis for the ligand-induced binding site formation remain unresolved. Here, we employ two absolute binding free energy methods, the double decoupling and the potential of mean force methods, to dissect the ligand-binding specificity in the c-MYC G-quadruplex. The calculated absolute binding free energies are in general agreement with the SPR result and suggest that quindoline has a slight preference for the 5′ site. The flanking residues around the two sites undergo significant reorganization as the ligand unbinds, which provides evidence for ligand-induced binding pocket formation. The results help interpret experimental data and inform rational design of small molecules targeting the c-MYC G-quadruplex

Quality measures of trained classifiers and comparison with publicly available prediction model [44].

<p>Abbreviations: TP – number of true positives, FN-false negatives, FP-false positives, TN-true negatives, ACC-accuracy, PRC-precision, SPC-specificity, MCC-Matthews correlation coefficient, Kappa-Kappa statistical value, RF-Random Forest method, NB- Naïve Bayes, J48-decision tree algorithm, SMO-Sequential Minimal Optimization.</p><p>Quality measures of trained classifiers and comparison with publicly available prediction model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110539#pone.0110539-Piippo1" target="_blank">[44]</a>.</p

Optimized parameter values for trained classifier.

<p>Optimized parameter values for trained classifier.</p

Volumes and position of the cavities at the NS3pro and NS3hel interface.

Positions of the cavities at the interface of the best aligned NS3pro and NS3hel to accommodate single stranded RNA (same as Fig 8D). Colored meshed areas mark the positions of cavities.</p