Structure-based deep learning for binding site detection in nucleic acid macromolecules

The prediction of RNA-small molecule binding sites in RNA structures based on geometric deep learning

Biological interactions between RNA and small-molecule ligands play a crucial role in determining the specific functions of RNA, such as catalysis and folding, and are essential for guiding drug design in the medical field. Accurately predicting the binding sites of ligands within RNA structures is therefore of significant importance. To address this challenge, we introduced a computational approach named RLBSIF (RNA-Ligand Binding Surface Interaction Fingerprints) based on geometric deep learning. This model utilizes surface geometric features, including shape index and distance-dependent curvature, combined with chemical features represented by atomic charge, to comprehensively characterize RNA-ligand interactions through MaSIF-based surface interaction fingerprints. Additionally, we employ the ResNet18 network to analyze these fingerprints for identifying ligand binding pockets. Trained on 440 binding pockets, RLBSIF achieves an overall pocket-level classification accuracy of 90 %. Through a full-space enumeration method, it can predict binding sites at nucleotide resolution. In two independent tests, RLBSIF outperformed competing models, demonstrating its efficacy in accurately identifying binding sites within complex molecular structures. This method shows promise for drug design and biological product development, providing valuable insights into RNA-ligand interactions and facilitating the design of novel therapeutic interventions. For access to the related source code, please visit RLBSIF on GitHub (https://github.com/ZUSTSTTLAB/RLBSIF).

Exploring chemical space for "druglike" small molecules in the age of AI

The announcement of 2024 Nobel Prize in Chemistry to Alphafold has reiterated the role of AI in biology and mainly in the domain of "drug discovery". Till few years ago, structure-based drug design (SBDD) has been the preferred experimental design in many academic and pharmaceutical R and D divisions for developing novel therapeutics. However, with the advent of AI, the drug design field especially has seen a paradigm shift in its R&D across platforms. If "drug design" is a game, there are two main players, the small molecule drug and its target biomolecule, and the rules governing the game are mainly based on the interactions between these two players. In this brief review, we will be discussing our efforts in improving the state-of-the-art technology with respect to small molecules as well as in understanding the rules of the game. The review is broadly divided into five sections with the first section introducing the field and the challenges faced and the role of AI in this domain. In the second section, we describe some of the existing small molecule libraries developed in our labs and follow-up this section with a more recent knowledge-based resource available for public use. In section four, we describe some of the screening tools developed in our laboratories and are available for public use. Finally, section five delves into how domain knowledge is improving the utilization of AI in drug design. We provide three case studies from our work to illustrate this work. Finally, we conclude with our thoughts on the future scope of AI in drug design.

Advances in structure-based allosteric drug design

The identification of allosteric binding sites forms a critical connection between structural and computational biology, substantially advancing the discovery of allosteric drugs. However, the prevailing strategies for allosteric drug development predominantly rely on high-throughput screening, which suffers from high failure rates due to a limited understanding of allosteric mechanisms. This review collects insights from case studies on allosteric mechanisms, protein structure databases and computation algorithm developments, aiming to enhance our comprehension of allostery and guide more effective allosteric drug development. A crucial element in this area is the integration of structural biology with computational biology, which is vital for translating three-dimensional structural datasets into available drug discovery knowledge. These datasets and AI algorithms underpin the establishment of the allosteric binding site identification leading to structure-activity relationships (SARs) and are fueling the development of computational algorithms tailored for allosteric proteins, thereby driving forward the field of allosteric drug discovery.

Predicting the Anti-SARS-CoV-2 Potential of Isoquinoline Alkaloids from Brazilian Siparunaceae Species Using Chemometric Tools

The COVID-19 pandemic has caused over 7 million deaths globally in the past four years. Siparuna spp. (Siparunaceae), which is used in Brazilian folk medicine, is considered a genus with potential antiviral alternatives. This study explored the correlation between phytochemicals in Siparuna leaf extracts (S. ficoides, S. decipiens, S. glycycarpa, S. reginae, and S. cymosa) and their potential against various SARS-CoV-2 targets. In vitro assays examined interactions between the spike protein and the ACE2 receptor, protease activity, and viral replication inhibition in Calu-3 cell models. UHPLC-MS/MS analysis, processed with MZmine and evaluated chemometrically, revealed isoquinoline alkaloids with bulbocapnine, showing promising therapeutic potential. Predictions regarding absorption, distribution, metabolism, excretion, and toxicity were conducted, along with molecular docking and dynamics simulations, to evaluate protein-ligand interaction stability. The results confirmed the antiviral activity of the Siparuna genus against SARS-CoV-2 targets, with 92% of the extracts maintaining over 70% cellular viability at 200 μg·mL-1 and 80% achieving more than 50% viral activity suppression at 50 μg·mL-1. These findings highlight the potential of isoquinoline alkaloids as novel anti-coronavirus agents and support the need for further exploration, isolation, and testing of Siparuna compounds in the fight against COVID-19.

Nucleic acid-responsive smart systems for controlled cargo delivery

Stimulus-responsive delivery systems allow controlled, highly regulated, and efficient delivery of various cargos while minimizing side effects. Owing to the unique properties of nucleic acids, including the ability to adopt complex structures by base pairing, their easy synthesis, high specificity, shape memory, and configurability, they have been employed in autonomous molecular motors, logic circuits, reconfigurable nanoplatforms, and catalytic amplifiers. Moreover, the development of nucleic acid (NA)-responsive intelligent delivery vehicles is a rapidly growing field. These vehicles have attracted much attention in recent years due to their programmable, controllable, and reversible properties. In this work, we review several types of NA-responsive controlled delivery vehicles based on locks and keys, including DNA/RNA-responsive, aptamer-responsive, and CRISPR-responsive, and summarize their advantages and limitations.

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Measuring the Enthalpy of an Individual Hydrogen Bond in a DNA Duplex with Nucleobase Isotope Editing and Variable-Temperature Infrared Spectroscopy

The level of interest in probing the strength of noncovalent interactions in DNA duplexes is high, as these weak forces dictate the range of suprastructures the double helix adopts under different conditions, in turn directly impacting the biological functions and industrial applications of duplexes that require making and breaking them to access the genetic code. However, few experimental tools can measure these weak forces embedded within large biological suprastructures in the native solution environment. Here, we develop experimental methods for detecting the presence of a single noncovalent interaction [a hydrogen bond (H-bond)] within a large DNA duplex in solution and measure its formation enthalpy (ΔH_f). We report that introduction of a H-bond into the TC2═O group from the noncanonical nucleobase 2-aminopurine produces an expected decrease ∼10 ± 0.76 cm^-1 (from ∼1720 cm^-1 in Watson-Crick to ∼1710 cm^-1 in 2-aminopurine), which correlates with an enthalpy of ∼0.93 ± 0.066 kcal/mol for this interaction.

RNA-targeted small-molecule drug discoveries: a machine-learning perspective

In the past two decades, machine learning (ML) has been extensively adopted in protein-targeted small molecule (SM) discovery. Once trained, ML models could exert their predicting abilities on large volumes of molecules within a short time. However, applying ML approaches to discover RNA-targeted SMs is still in its early stages. This is primarily because of the intrinsic structural instability of RNA molecules that impede the structure-based screening or designing of RNA-targeted SMs. Recently, with more studies revealing RNA structures and a growing number of RNA-targeted ligands being identified, it resulted in an increased interest in the field of drugging RNA. Undeniably, intracellular RNA is much more abundant than protein and, if successfully targeted, will be a major alternative target for therapeutics. Therefore, in this context, as well as under the premise of having RNA-related research data, ML-based methods can get involved in improving the speed of traditional experimental processes. [Figure: see text].

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.