MicroED for the study of protein–ligand interactions and the potential for drug discovery

Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery

The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-protein structures in directing drug discovery; (2) to present examples of drugs from the recent literature (past decade) of case studies where such strategies have been applied to accelerate the discovery pipeline. Compared to 50 years ago when drug discovery was largely a synthetic chemist driven research exercise, today a holistic approach needs to be adopted with seamless integration between synthetic and medicinal chemistry, supramolecular complexes, computations, artificial intelligence, machine learning, structural biology, chemical biology, diffraction analytical tools, drugs databases, and pharmacology. The urgency for an integrated and collaborative platform to accelerate drug discovery in an academic setting is emphasized.

MicroED as a Powerful Tool for Structure Determination of Macrocyclic Drug Compounds Directly from Their Powder Formulations

Macrocycles are important drug leads with many advantages including the ability to target flat and featureless binding sites as well as to act as molecular chameleons and thereby reach intracellular targets. However, due to their complex structures and inherent flexibility, macrocycles are difficult to study structurally, and there are limited structural data available. Herein, we use the cryo-EM method MicroED to determine the novel atomic structures of several macrocycles that have previously resisted structural determination. We show that structures of similar complexity can now be obtained rapidly from nanograms of material and that different conformations of flexible compounds can be derived from the same experiment. These results will have an impact on contemporary drug discovery as well as natural product exploration.

Structure determination of a DNA crystal by MicroED

Microcrystal electron diffraction (MicroED) is a powerful tool for determining high-resolution structures of microcrystals from a diverse array of biomolecular, chemical, and material samples. In this study, we apply MicroED to DNA crystals, which have not been previously analyzed using this technique. We utilized the d(CGCGCG)2 DNA duplex as a model sample and employed cryo-FIB milling to create thin lamella for diffraction data collection. The MicroED data collection and subsequent processing resulted in a 1.10 Å resolution structure of the d(CGCGCG)2 DNA, demonstrating the successful application of cryo-FIB milling and MicroED to the investigation of nucleic acid crystals.

MicroED as a powerful tool for structure determination of macrocyclic drug compounds directly from their powder formulations

Macrocycles are important drug leads with many advantages including the ability to target flat and featureless binding sites as well as act as molecular chameleons and thereby reach intracellular targets. However, due to their complex structures and inherent flexibility, macrocycles are difficult to study structurally and there are limited structural data available. Herein, we use the cryo-EM method MicroED to determine the novel atomic structures of several macrocycles which have previously resisted structural determination. We show that structures of similar complexity can now be obtained rapidly from nanograms of material, and that different conformations of flexible compounds can be derived from the same experiment. These results will have impact on contemporary drug discovery as well as natural product exploration.

Structure determination of a DNA crystal by MicroED

Microcrystal electron diffraction (MicroED) is a powerful tool for determining high-resolution structures of microcrystals from a diverse array of biomolecular, chemical, and material samples. In this study, we apply MicroED to DNA crystals, which have not been previously analyzed using this technique. We utilized the d(CGCGCG) 2 DNA duplex as a model sample and employed cryo-FIB milling to create thin lamella for diffraction data collection. The MicroED data collection and subsequent processing resulted in a 1.10 Å resolution structure of the d(CGCGCG) 2 DNA, demonstrating the successful application of cryo-FIB milling and MicroED to the investigation of nucleic acid crystals.

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism

MOTIVATION

Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.

RESULTS

In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications.

AVAILABILITY AND IMPLEMENTATION

The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cryo-electron tomography related radiation-damage parameters for individual-molecule 3D structure determination

To understand the dynamic structure-function relationship of soft- and biomolecules, the determination of the three-dimensional (3D) structure of each individual molecule (nonaveraged structure) in its native state is sought-after. Cryo-electron tomography (cryo-ET) is a unique tool for imaging an individual object from a series of tilted views. However, due to radiation damage from the incident electron beam, the tolerable electron dose limits image contrast and the signal-to-noise ratio (SNR) of the data, preventing the 3D structure determination of individual molecules, especially at high-resolution. Although recently developed technologies and techniques, such as the direct electron detector, phase plate, and computational algorithms, can partially improve image contrast/SNR at the same electron dose, the high-resolution structure, such as tertiary structure of individual molecules, has not yet been resolved. Here, we review the cryo-electron microscopy (cryo-EM) and cryo-ET experimental parameters to discuss how these parameters affect the extent of radiation damage. This discussion can guide us in optimizing the experimental strategy to increase the imaging dose or improve image SNR without increasing the radiation damage. With a higher dose, a higher image contrast/SNR can be achieved, which is crucial for individual-molecule 3D structure. With 3D structures determined from an ensemble of individual molecules in different conformations, the molecular mechanism through their biochemical reactions, such as self-folding or synthesis, can be elucidated in a straightforward manner.

Crystalline Sponge Method by Three-Dimensional Electron Diffraction

The crystalline sponge method has shown to be a novel strategy for the structure determination of noncrystalline, oily, or trace amount of a compound. A target compound was absorbed and oriented orderly in the pregrown porous crystal for x-ray diffraction analysis. However, the diffusion in the micron-sized crystals is rather difficult. Lots of trial-and-error experiments are needed to optimize the guest-soaking process and to improve data quality. Nanocrystals are better in diffusion, yet it could not conduct a single crystal x-ray diffraction (SCXRD) analysis. Three-dimensional electron diffraction (3D-ED) is a powerful diffraction tool for the structure determination of small crystals. In this work, we successfully carried out the crystalline sponge method by 3D-ED technique using {(ZnI2)3-[2,4,6-tris(4-pyridyl)-1,3,5-triazine]2·x(guest)}n (1-Guest) porous complex nanocrystals. On account of the better diffuse ability of nanocrystals, the time needed for solvent exchange and guest soaking protocols are shortened 50-fold faster versus the original protocol. The crystal structure of the crystalline sponge incorporated with three different guests was fully resolved using a merged dataset. The structure model was identical to previously reported ones using x-ray, showing that the accuracy of the 3D-ED was comparable with SCXRD. The refinement results can also give the precise occupancy of the guest molecule soaked in the porous crystal. This work not only provides a new data collection strategy for crystalline sponge method but also demonstrates the potential of 3D-ED techniques to study host-guest interaction by solving the fine structure of porous material.