Systematic analysis and comparison of peptide specificity and selectivity between their cognate receptors and noncognate decoys.

Protein-peptide interactions (PpIs) play an important role in cell signaling networks and have been exploited as new and attractive therapeutic targets. The affinity and specificity are two unity-of-opposite aspects of PpIs (and other biomolecular interactions); the former indicates the absolute binding strength between the peptide ligand and its cognate protein receptor in a PpI, while the latter represents the relative recognition selectivity of the peptide ligand for its cognate protein receptor in a PpI over those noncognate decoys that could be potentially encountered by the peptide in cell. Although the PpI binding affinity has been widely investigated over the past decades, the peptide recognition specificity (and selectivity) still remains largely unexplored to date.

PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships.

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures.

Targeting peptide-mediated interactions in omics.

Peptide-mediated interactions (PMIs) play a crucial role in cell signaling network, which are responsible for about half of cellular protein-protein associations in the human interactome and have recently been recognized as a new kind of promising druggable target for drug development and disease therapy. In this article, we give a systematic review regarding the proteome-wide discovery of PMIs and targeting druggable PMIs (dPMIs) with chemical drugs, self-inhibitory peptides (SIPs) and protein agents, particularly focusing on their implications and applications for therapeutic purpose in omics. We also introduce computational peptidology strategies used to model, analyze, and design PMI-targeted molecular entities and further extend the concepts of protein context, direct/indirect readout, and enthalpy/entropy effect involved in PMIs.

The regular use of calcium channel blockers before flexible URS appears to facilitate primary UAS insertion: a retrospective study in a single center.

Purpose: To evaluate the effect of regular use of CCB before flexible URS for successful primary UAS insertion.

Materials And Methods: We retrospectively analyzed 209 patients who underwent flexible ureteroscopy (URS) for upper urinary tract calculi between Jan 2021 and Dec 2021. Patients were divided into two groups based on whether calcium channel blockers (CCB) were used (n = 72) or not (n = 137).

Comparison of pre-indwelling double-J stents versus ureteral catheters for artificial hydronephrosis in percutaneous nephrolithotomy: A retrospective cohort study.

Purpose: To compare the clinical efficacy and safety of pre-indwelling double-J stents versus ureteral catheters for artificial hydronephrosis in percutaneous nephrolithotomy (PCNL).

Materials And Methods: We retrospectively analyzed the data of 1,258 patients who underwent PCNL for kidney stones from August 2017 to July 2020 in our hospital. Among them, 682 patients had double-J stents inserted (DJ group) and 576 patients had ureteral catheters (UC group).

Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level.

Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein-protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom-residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised-supervised PpI affinity (usPpIA) predictor.