On the Quest of Small Molecules that Can Mimic Psalmotoxin-1, a Powerful Peptidic Modulator of the Acid Sensing Channel ASIC1a

Acid-sensing ion channels (ASICs) are thought to play a key role in a number of pathologies, from neuronal injury to pain sensation, while no drug has yet been approved as their modulator. This work was devised to asses roughly, yet from first principles, the relative energies of binding in the most important acidic pocket of cASIC1a, thereby paving the way to find small molecules that can mimic Pctx1, the most powerful peptidic modulator of cASIC1a. To this end, MD simulations for the overall conformation, and QM-MM simulations specifically for the location of hydrogen atoms, allowed disentangling the relative weight of the various non-bonded interactions between PcTx1 and cASIC1a. Main weight could be attributed to deeply buried salt bridges formed by the guanidinium end chains of residues Arg26 and Arg27 on PcTx1 and carboxylate end chain of distant residues Asp and Glu on cASIC1a. Rewardingly, in a preliminary attempt at exploiting these observations toward a small-molecule modulator, a Arg26-Arg27 stretch, excised from the PcTx1-cASIC1a complex and slightly simplified, on automated rigid docking on ligand-free receptor was observed to form most of the above salt bridges.

Structural insights and evaluation of the potential impact of missense variants on the interactions of SLIT2 with ROBO1/4 in cancer progression

The cognate interaction of ROBO1/4 with its ligand SLIT2 is known to be involved in lung cancer progression. However, the precise role of genetic variants, disrupting the molecular interactions is less understood. All cancer-associated missense variants of ROBO1/4 and SLIT2 from COSMIC were screened for their pathogenicity. Homology modelling was done in Modeller 9.17, followed by molecular simulation in GROMACS. Rigid docking was performed for the cognate partners in PatchDock with refinement in HADDOCK server. Post-docking alterations in conformational, stoichiometric, as well as structural parameters, were assessed. The disruptive variants were ranked using a weighted scoring scheme. In silico prioritisation of 825 variants revealed 379 to be potentially pathogenic out of which, about 12% of the variants, i.e. ROBO1 (14), ROBO4 (8), and SLIT2 (23) altered the cognate docking. Six variants of ROBO1 and 5 variants of ROBO4 were identified as "high disruptors" of interactions with SLIT2 wild type. Likewise, 17 and 13 variants of SLIT2 were found to be "high disruptors" of its interaction with ROBO1 and ROBO4, respectively. Our study is the first report on the impact of cancer-associated missense variants on ROBO1/4 and SLIT2 interactions that might be the drivers of lung cancer progression.

How ‘Protein-Docking’ Translates into the New Emerging Field of Docking Small Molecules to Nucleic Acids?

In this review, we retraced the ‘40-year evolution’ of molecular docking algorithms. Over the course of the years, their development allowed to progress from the so-called ‘rigid-docking’ searching methods to the more sophisticated ‘semi-flexible’ and ‘flexible docking’ algorithms. Together with the advancement of computing architecture and power, molecular docking’s applications also exponentially increased, from a single-ligand binding calculation to large screening and polypharmacology profiles. Recently targeting nucleic acids with small molecules has emerged as a valuable therapeutic strategy especially for cancer treatment, along with bacterial and viral infections. For example, therapeutic intervention at the mRNA level allows to overcome the problematic of undruggable proteins without modifying the genome. Despite the promising therapeutic potential of nucleic acids, molecular docking programs have been optimized mostly for proteins. Here, we have analyzed literature data on nucleic acid to benchmark some of the widely used docking programs. Finally, the comparison between proteins and nucleic acid targets docking highlighted similarity and differences, which are intrinsically related to their chemical and structural nature.

A Practical Perspective: The Effect of Ligand Conformers on the Negative Image-Based Screening

Negative image-based (NIB) screening is a rigid molecular docking methodology that can also be employed in docking rescoring. During the NIB screening, a negative image is generated based on the target protein’s ligand-binding cavity by inverting its shape and electrostatics. The resulting NIB model is a drug-like entity or pseudo-ligand that is compared directly against ligand 3D conformers, as is done with a template compound in the ligand-based screening. This cavity-based rigid docking has been demonstrated to work with genuine drug targets in both benchmark testing and drug candidate/lead discovery. Firstly, the study explores in-depth the applicability of different ligand 3D conformer generation software for acquiring the best NIB screening results using cyclooxygenase-2 (COX-2) as the example system. Secondly, the entire NIB workflow from the protein structure preparation, model build-up, and ligand conformer generation to the similarity comparison is performed for COX-2. Accordingly, hands-on instructions are provided on how to employ the NIB methodology from start to finish, both with the rigid docking and docking rescoring using noncommercial software. The practical aspects of the NIB methodology, especially the effect of ligand conformers, are discussed thoroughly, thus, making the methodology accessible for new users.

In-silico Study of Sulfonylurea Family Members’ Binding Site with their Receptor Kir6.2\SUR1

Sulfonylurea family members have been used as a second preferred line in the treatment of Type II Diabetes Mellitus (TIIDM) for decades. Only one crystal structure for its receptor Kir6.2\SUR1 binding with one of sulfonylurea member; Glibenclamide (GBM), is available in Protein Data Bank (PDB) database. The aim of this manuscript is to study in-silico other sulfonylurea family members’ interactions with their receptor Kir6.2\SUR1 using a docking software in the default settings. We have checked the validity of the software for the study. Then, we have applied a rigid docking on 14 compounds of sulfonyl urea group which they have anti-hyperglycemia activity. Next, we have compared their interactions to GBM interactions with Kir6.2\SUR1.  As a result, many compounds of this family had bound to Kir6.2\SUR1 receptor in the same pocket as GBM. These results confirmed a perspective we have discussed about sulfonylurea structure activity relationship.

Paratope Prediction using Convolutional and Recurrent Neural Networks

Antibodies play an essential role in the immune system of vertebrates and are vital tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method outperforms the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. The Parapred method is freely available at https://github.com/eliberis/parapred for download.