Protein-RNA Docking Using ICM

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

Developing a comprehensive solution aimed to disrupt LARS1/RagD protein-protein interaction

Aminoacyl-tRNA synthetases are crucial enzymes involved in protein synthesis and various cellular physiological reactions. Aside from their standard role in linking amino acids to the corresponding tRNAs, they also impact protein homeostasis by controlling the level of soluble amino acids within the cell. For instance, leucyl-tRNA synthetase (LARS1) acts as a leucine sensor for the mammalian target of rapamycin complex 1 (mTORC1), and may also function as a probable GTPase-activating protein (GAP) for the RagD subunit of the heteromeric activator of mTORC1. In turn, mTORC1 regulates cellular processes, such as protein synthesis, autophagy, and cell growth, and is implicated in various human diseases including cancer, obesity, diabetes, and neurodegeneration. Hence, inhibitors of mTORC1 or a deregulated mTORC1 pathway may offer potential cancer therapies. In this study, we investigated the structural requirements for preventing the sensing and signal transmission from LARS to mTORC1. Building upon recent studies on mTORC1 regulation activation by leucine, we lay the foundation for the development of chemotherapeutic agents against mTORC1 that can overcome resistance to rapamycin. Using a combination of in-silico approaches to develop and validate an alternative interaction model, discussing its benefits and advancements. Finally, we identified a set of compounds ready for testing to prevent LARS1/RagD protein-protein interactions. We establish a basis for creating chemotherapeutic drugs targeting mTORC1, which can conquer resistance to rapamycin. We utilize in-silico methods to generate and confirm an alternative interaction model, outlining its advantages and improvements, and pinpoint a group of novel substances that can prevent LARS1/RagD interactions.Communicated by Ramaswamy H. Sarma.

Challenges in structural modeling of RNA-protein interactions

In the past few years, the number of RNA-binding proteins (RBP) and RNA-RBP interactions has increased significantly. Here, we review recent developments in the methodology for protein-RNA and protein-protein complex structure modeling with deep learning and co-evolution, as well as discuss the challenges and opportunities for building a reliable approach for protein-RNA complex structure modelling. Protein Data bank (PDB) and Cross-linking immunoprecipitation (CLIP) data could be combined together and used to infer 2D geometry of protein-RNA interactions by deep learning.

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

Evaluating native-like structures of RNA-protein complexes through the deep learning method

RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53-15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes.

Characterization of the Gateway Decarboxylase for Psilocybin Biosynthesis

The l-tryptophan decarboxylase PsiD catalyzes the initial step of the metabolic cascade to psilocybin, the major indoleethylamine natural product of the "magic" mushrooms and a candidate drug against major depressive disorder. Unlike numerous pyridoxal phosphate (PLP)-dependent decarboxylases for natural product biosyntheses, PsiD is PLP-independent and resembles type II phosphatidylserine decarboxylases. Here, we report on the in vitro biochemical characterization of Psilocybe cubensis PsiD along with in silico modeling of the PsiD structure. A non-canonical serine protease triad for autocatalytic cleavage of the pro-protein was predicted and experimentally verified by site-directed mutagenesis.

Molecular docking studies, in-silico ADMET predictions and synthesis of novel PEGA-nucleosides as antimicrobial agents targeting class B1 metallo-β-lactamases

Class B1 metallo-β-lactamases (MBLs) are metalloenzymes found in drug resistant bacteria. The enzyme requires zinc ions, along with conserved amino acid coordination for nucleophilic attack of the lactam ring to induce hydrolysis and inactivation of β-lactam and some carbapenem antibiotics. To this date there are no clinically relevant class B1 MBL inhibitors, however L-captopril has shown significant results against NDM-1, the most difficult MBL to inhibit. Herein, we report the synthesis and evaluation of novel nucleoside analogues modified with polyethylene glycolamino (PEGA) as potential inhibitors for class B1 MBLs. Molecular dynamics simulations, using internal coordinate mechanics (ICM) algorithm, were performed on subclass B1 enzyme complex models screened with twenty-one possible PEGA-nucleosides. Analogue A, 3'-deoxy-3'-(2-(2-hydroxyethoxy)ethanamino)-β-D-xylofuranosyluracil showed superior binding, with high specificity to the conserved zinc ions in the class B1 MBL active site by utilizing key β-lactam mimic points in the uridine nucleobase. The PEGA moiety showed chelating activity with zinc and disrupted the metal-binding amino acid geometry. In all subclass B1 proteins tested, analogue A had the most effective inhibition when compared to penicillin or L-captopril. Chemical synthesis was performed by condensation of the corresponding keto ribonucleoside with PEGA, followed by enantioselective reduction of the formed imine to produce the amino derivative with desired configuration. Pharmacokinetic and pharmacodynamic screenings revealed that PEGA-pyrimidine nucleosides are not toxic, nor violate Lipinski's rules. These results suggested that analogue A can be proposed as a potential metalloenzyme inhibitor against the widespread antibiotic resistant bacteria and is worth further in vitro and in vivo investigations.

P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking

MOTIVATION

The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy.

RESULTS

In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html.

AVAILABILITY AND IMPLEMENTATION

www.rnabinding.com/P3DOCK/P3DOCK.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models

MOTIVATION

Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).

RESULTS

Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/mprdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein-assisted RNA fragment docking (RnaX) for modeling RNA-protein interactions using ModelX

Protein–RNA interactions, key in biological processes, remained refractory to prediction algorithms. Here we present a new extension of the ModelX tool suite designed for this purpose. RNA–protein complexes in the Protein Data Bank were decomposed into small peptide–oligonucleotide interacting fragment pairs and used as building blocks to assemble big scaffolds representing complex RNA–protein interactions. This method has already been successful for designing DNA–protein and protein–protein interfaces. Areas under the curve up to 0.86 were achieved on binding site prediction showing the accuracy and coverage of our approach over established and in-house benchmarking sets. Together with FoldX protein design tool suite we were able to engineer backbone- and side chain-compatible interfaces using naked protein structures as input.

RNA–protein interactions are crucial for such key biological processes as regulation of transcription, splicing, translation, and gene silencing, among many others. Knowing where an RNA molecule interacts with a target protein and/or engineering an RNA molecule to specifically bind to a protein could allow for rational interference with these cellular processes and the design of novel therapies. Here we present a robust RNA–protein fragment pair-based method, termed RnaX, to predict RNA-binding sites. This methodology, which is integrated into the ModelX tool suite (http://modelx.crg.es), takes advantage of the structural information present in all released RNA–protein complexes. This information is used to create an exhaustive database for docking and a statistical forcefield for fast discrimination of true backbone-compatible interactions. RnaX, together with the protein design forcefield FoldX, enables us to predict RNA–protein interfaces and, when sufficient crystallographic information is available, to reengineer the interface at the sequence-specificity level by mimicking those conformational changes that occur on protein and RNA mutagenesis. These results, obtained at just a fraction of the computational cost of methods that simulate conformational dynamics, open up perspectives for the engineering of RNA–protein interfaces.

Computational approaches to macromolecular interactions in the cell

Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.

Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.