Structural and functional characterization of binding sites in metallocarboxypeptidases based on Optimal Docking Area analysis

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Characterization of voltage-dependent calcium channel blocking peptides from the venom of the tarantula Grammostola rosea

Voltage-dependent calcium channel blocking peptides were purified and sequenced from the venom of the tarantula, Grammostola rosea. cDNAs encoding the peptide sequences were cloned from the venom gland cDNA library. The electrophysiological effects of the peptides on several types of voltage-dependent calcium channels were evaluated using a Xenopus laevis oocyte expression system. A peptide contained in one of the HPLC peak fractions inhibited P/Q type voltage-dependent calcium channels (Ca(v)2.1). The amino acid sequence of this peptide is identical to that of ω-grammotoxin SIA. A peptide from another discrete peak, which is identical to GsAFII except for one tryptophan residue in the C-terminus, inhibited L-type voltage-dependent calcium channels (Ca(v)1.2). A novel peptide, named GTx1-15 (Accession number, AB201016), shows 76.5% sequence homology with the sodium channel blocker phrixotoxin 3, however, GTx1-15 preferentially inhibited T-type voltage-dependent calcium channels (Ca(v)3.1). In silico secondary and tertiary structure prediction revealed that GTx1-15 and sodium channel blockers such as hainantoxin-IV, phrixotoxin 3, and ceratotoxin 2 show very similar β-strand composition, distribution of Optimal Docking Areas (continuous surface patches likely to be involved in protein-protein interactions), and surface electrostatic potential. These findings suggest that these peptide toxins evolved from common ancestors by gene duplication to maintain surface atmospheres appropriate for interaction with low-voltage-dependent ion channels.

Scoring by intermolecular pairwise propensities of exposed residues (SIPPER): a new efficient potential for protein-protein docking

A detailed and complete structural knowledge of the interactome is one of the grand challenges in Biology, and a variety of computational docking approaches have been developed to complement experimental efforts and help in the characterization of protein-protein interactions. Among the different docking scoring methods, those based on physicochemical considerations can give the maximum accuracy at the atomic level, but they are usually computationally demanding and necessarily noisy when implemented in rigid-body approaches. Coarser-grained knowledge-based potentials are less sensitive to details of atomic arrangements, thus providing an efficient alternative for scoring of rigid-body docking poses. In this study, we have extracted new statistical potentials from intermolecular pairs of exposed residues in known complex structures, which were then used to score protein-protein docking poses. The new method, called SIPPER (scoring by intermolecular pairwise propensities of exposed residues), combines the value of residue desolvation based on solvent-exposed area with the propensity-based contribution of intermolecular residue pairs. This new scoring function found a near-native orientation within the top 10 predictions in nearly one-third of the cases of a standard docking benchmark and proved to be also useful as a filtering step, drastically reducing the number of docking candidates needed by energy-based methods like pyDock.