Modelling the binding mode of macrocycles: Docking and conformational sampling

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Simulation Reveals the Chameleonic Behavior of Macrocycles

Conformational analysis is central to the design of bioactive molecules. It is particularly challenging for macrocycles due to noncovalent transannular interactions, steric interactions, and ring strain that are often coupled. Herein, we simulated the conformations of five macrocycles designed to express a progression of increasing complexity in environment-dependent intramolecular interactions and verified the results against NMR measurements in chloroform and dimethyl sulfoxide. Molecular dynamics using an explicit solvent model, but not the Monte Carlo method with implicit solvation, handled both solvents correctly. Refinement of conformations at the ab initio level was fundamental to reproducing the experimental observations─standard state-of-the-art molecular mechanics force fields were insufficient. Our simulations correctly predicted the intramolecular interactions between side chains and the macrocycle and revealed an unprecedented solvent-induced conformational switch of the macrocyclic ring. Our results provide a platform for the rational, prospective design of molecular chameleons that adapt to the properties of the environment.

Dynamic Docking of Macrocycles in Bound and Unbound Protein Structures with DynaDock

Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.

Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions

Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding the training set, developing physically meaningful features, employing our recently developed linear empirical scoring function Lin_F9 (Yang, C. J. Chem. Inf. Model. 2021, 61, 4630-4644) as the baseline, and applying extreme gradient boosting (XGBoost) with Δ-machine learning, we have further improved the robustness and applicability of machine-learning scoring functions. Besides the top performances for scoring-ranking-screening power tests of the CASF-2016 benchmark, the new scoring function Δ_{Lin_F9}XGB also achieves superior scoring and ranking performances in different structure types that mimic real docking applications. The scoring powers of Δ_{Lin_F9}XGB for locally optimized poses, flexible redocked poses, and ensemble docked poses of the CASF-2016 core set achieve Pearson's correlation coefficient (R) values of 0.853, 0.839, and 0.813, respectively. In addition, the large-scale docking-based virtual screening test on the LIT-PCBA data set demonstrates the reliability and robustness of Δ_{Lin_F9}XGB in virtual screening application. The Δ_{Lin_F9}XGB scoring function and its code are freely available on the web at (https://yzhang.hpc.nyu.edu/Delta_LinF9_XGB).

Performance evaluation of flexible macrocycle docking in AutoDock

Macrocycles represent an important class of ligands, both in natural products and designed drugs. In drug design, macrocyclizations can impart specific ligand conformations and contribute to passive permeation by encouraging intramolecular H-bonds. AutoDock-GPU and Vina can model macrocyclic ligands flexibly, without requiring the enumeration of macrocyclic conformers before docking. Here, we characterize the performance of the method for handling macrocyclic compounds, which is implemented and the default behaviour for ligand preparation with our ligand preparation pipeline, Meeko. A pseudoatom is used to encode bond geometry and produce an anisotropic closure force for macrocyclic rings. This method is evaluated on a diverse set of small molecule and peptide macrocycles, ranging from 7- to 33-membered rings, showing little accuracy loss compared to rigid redocking of the X-ray macrocycle conformers. This suggests that for conformationally flexible macrocycles with unknown binding modes, this method can be effectively used to predict the macrocycle conformation.

Automated Design of Macrocycles for Therapeutic Applications: From Small Molecules to Peptides and Proteins

Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein surfaces, and otherwise have favorable properties. Macrocyclization of a known binder may stabilize its bioactive conformation and improve its metabolic stability, cell permeability, and in certain cases oral bioavailability. Herein, we present implementation and application of an approach that automatically generates, evaluates, and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as the starting point, this approach identifies attachment points, generates linkers, evaluates their geometric compatibility, and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROteolysis TArgeting Chimeras (PROTACs) and proteins.