Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Molecular Docking and Molecular Dynamics Simulations in Related to Leishmania donovani: An Update and Literature Review

Leishmaniasis, a disease caused by Leishmania parasites and transmitted via sandflies, presents in two main forms: cutaneous and visceral, the latter being more severe. With 0.7 to 1 million new cases each year, primarily in Brazil, diagnosing remains challenging due to diverse disease manifestations. Traditionally, the identification of Leishmania species is inferred from clinical and epidemiological data. Advances in disease management depend on technological progress and the improvement of parasite identification programs. Current treatments, despite the high incidence, show limited efficacy due to factors like cost, toxicity, and lengthy regimens causing poor adherence and resistance development. Diagnostic techniques have improved but a significant gap remains between scientific progress and application in endemic areas. Complete genomic sequence knowledge of Leishmania allows for the identification of therapeutic targets. With the aid of computational tools, testing, searching, and detecting affinity in molecular docking are optimized, and strategies that assess advantages among different options are developed. The review focuses on the use of molecular docking and molecular dynamics (MD) simulation for drug development. It also discusses the limitations and advancements of current treatments, emphasizing the importance of new techniques in improving disease management.

CHARMM-GUI Ligand Designer for Template-Based Virtual Ligand Design in a Binding Site

Rational drug design involves a task of finding ligands that would bind to a specific target protein. This work presents CHARMM-GUI Ligand Designer that is an intuitive and interactive web-based tool to design virtual ligands that match the shape and chemical features of a given protein binding site. Ligand Designer provides ligand modification capabilities with 3D visualization that allow researchers to modify and redesign virtual ligands while viewing how the protein-ligand interactions are affected. Virtual ligands can also be parameterized for further molecular dynamics (MD) simulations and free energy calculations. Using 8 targets from 8 different protein classes in the directory of useful decoys, enhanced (DUD-E) data set, we show that Ligand Designer can produce similar ligands to the known active ligands in the crystal structures. Ligand Designer also produces stable protein-ligand complex structures when tested using short MD simulations. We expect that Ligand Designer can be a useful and user-friendly tool to design small molecules in any given potential ligand binding site on a protein of interest.

A systematic analysis of protein-carbohydrate interactions in the Protein Data Bank

Protein-carbohydrate interactions underlie essential biological processes. Elucidating the mechanism of protein-carbohydrate recognition is a prerequisite for modeling and optimizing protein-carbohydrate interactions, which will help in discovery of carbohydrate-derived therapeutics. In this work, we present a survey of a curated database consisting of 6,402 protein-carbohydrate complexes in the Protein Data Bank (PDB). We performed an all-against-all comparison of a subset of nonredundant binding sites, and the result indicates that the interaction pattern similarity is not completely relevant to the binding site structural similarity. Investigation of both binding site and ligand promiscuities reveals that the geometry of chemical feature points is more important than local backbone structure in determining protein-carbohydrate interactions. A further analysis on the frequency and geometry of atomic interactions shows that carbohydrate functional groups are not equally involved in binding interactions. Finally, we discuss the usefulness of protein-carbohydrate complexes in the PDB with acknowledgement that the carbohydrates in many structures are incomplete.