Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community.

The correct functioning of organisms heavily relies on proteins and RNAs, which hold significant roles in governing biological pathways and mechanisms from a molecular perspective, as well as controlling cell function and transmitting genetic information [1]. The research focused on RNA and proteins is currently experiencing a surge in popularity. Significant strides have been made to comprehend the intricacies of protein and RNA structure and function through computational and experimental techniques [2–6]. Researchers aim to identify the associations of functions or diseases from the proteins and nucleic acids. Once the structure is accessible, analyzing the three-dimensional information for annotating functions more accurately becomes possible. Through the integration of experiments, this approach provides an invaluable understanding of biological mechanisms and propels the progress of therapeutic strategies for various diseases. Recent technological advancements have led to an improved version of virus structures and facilitated the development of vaccines, particularly in the ongoing COVID-19 pandemic [7]. Besides, targeting human protein and RNA through drug treatments presents a promising solution for curing cancer. Despite their potential benefits, concerns remain regarding the risk of cardiac toxicity and associated high recurrence rates. This has prompted the need for innovative techniques to gain a deeper understanding of protein and RNA structures and functions. This special issue centers around innovative experimental and computational biological, chemical and medical research methods. Researchers from multidisciplinary backgrounds share their expertise in modeling proteins and RNAs. They utilize distinct strategies to gain novel functional insights into critical biological systems and help solve unanswered challenging questions relevant to health and disease. This topic encompasses diverse groundbreaking experimental techniques, integrating theoretical simulations and cutting-edge computational. Collectively, these elements contribute to advancing the modeling of RNA and proteins. It is still difficult to precisely determine the structure and dynamics of complex systems through experimental methods. For example, accurately constructing atomic structures of complexes from cryo-EM maps remains challenging [8]. In the article ‘DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting’, Zhang et al. [9] introduce an automated method for constructing protein complex models from cryo-EM maps. This approach involves an iterative assembly procedure that uses fast quasi-Newton optimization and Differential Evolution algorithms to combine chain- and domain-level matching and fitting for predicted chain models. Their results highlight DEMO-EM2 as an efficient method to provide a reliable cryo-EM complex structure. In the article ‘Enhancing protein dynamics analysis with hydrophilic polyethylene glycol cross-linkers’, Sun et al. [10] utilized XL-MS experimental techniques to analyze structural complexes’ dynamics accurately. They compared two cross-linkers with different backbone properties: the hydrophilic BS(PEG)2 and hydrophobic DSS/BS3. They assessed their ability to capture protein structure and dynamics through in vitro, in silico and in vivo experiments. Their findings from in vitro and in vivo cross-linking experiments strongly support the enhanced capability of BS(PEG)2 in capturing the dynamic structural nuances of multi-domain proteins. All-atom molecular dynamic (MD) simulations and quantum chemical calculation further highlight BS(PEG)2 intrinsic attributes, including heightened hydrophilicity and increased polarity, which facilitate its proximity to the protein surface. These two works have vastly improved experimental techniques for determining complex structures and dynamic changes. Their valuable insights and contributions have propelled experimental determinations to new heights for applications. MD technology heavily relies on precise energy potentials to analyze the physical movements of atoms and molecules. When conducting MD simulations, transitioning between states separated by considerable energy barriers can be difficult. In the article ‘Differentiable rotamer sampling with molecular force fields’, Sha et al. [11] have developed a mathematical foundation to overcome this limitation using Boltzmann generators. To address the biases in angle generation and practical deficiencies of Boltzmann generators, they have introduced physics-based geometric methods that allow for unbiased and direct sampling of the rotameric conformations of proteins and RNAs. They demonstrate that the Boltzmann generator approach helps generate biologically relevant conformations faster and more accurately than traditional MD methods. Measuring long-range electrostatic potential poses a challenge when extending MD calculations to larger time scales. In the article, ‘A novel approach to study multi-domain motions in JAK1’s activation mechanism based on energy landscape’, Sun et al. [12] introduced a novel electrostatic potential calculation tool, Delphi. They successfully applied this method to investigate multi-domain motions in the activation mechanism of JAK1. This study presents a promising approach to studying long-range electrostatic potential, a typical example of resolving energy barriers. In the article ‘Computational insights into the cross-talk between medin and Aβ: implications for age-related vascular risk factors in Alzheimer’s disease’, Huang et al. [13] employed atomistic discrete molecular dynamics (DMD) simulations to study protein folding and aggregation. DMD is a highly efficient method that simplifies the energy landscape by coarse-graining it instead of integrating continuous energetic potentials [14]. This approach significantly saves time and computational resources, making it an ideal solution for energy calculations of large-scale and long-term MD simulations. The authors have systematically investigated the self-association, co-aggregation and cross-seeding phenomenon between medin and amyloid-β protein in Alzheimer’s disease, utilizing the benefits of DMD to gain valuable insights into these complex mechanisms. These three works have broadened the usability of MD simulation in the analysis of complex cellular processes and mechanisms, providing innovative, fast and accurate solutions to overcome MD limitations. The special issue also focuses on developing cutting-edge AI computational methodologies for complex structure prediction. One of the areas that require improvement is the accurate binding site prediction. In the article ‘Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis’, Xu et al. [15] proposed network methods to analyze the experimental binding targets. They conducted a large-scale systematic study of the histone interactions network. Their works have identified the preferred binding hotspots on nucleosomal/linker DNA and histone octamer and revealed diverse binding modes between nucleosome and binding partners. The predicted recurrent cancer mutations have significant disruptive effects on histone/nucleosome interactions and may have driver status in the development of cancers. In the article ‘RNet: a network strategy to predict RNA binding preferences’, Liu et al. [16] developed a machine-learning-based network approach, RNet, to predict RNA binding sites and dynamical binding behavior. The binding site identification algorithm integrates local and global network properties and demonstrates remarkable precision and robustness regarding perturbations. Additionally, the highly effective distance-based dynamical graph algorithm considers geometric network properties to characterize RNA complex interface binding behavior accurately and outperforms the traditional method by 30%. Their frontier interpretable network prediction algorithm, RNet, demonstrates the best efficiency and precision. RNet positively impacts complex structure prediction, leading to a significant increase in the number of successful hits. This eliminates the need for time-consuming screening of thousands of potential binding candidates, making it an essential tool for efficient and practical research. An additional limitation is to predict the binding structure accurately [17, 18]. In the article ‘Drug repositioning based on weighted local information augmented graph neural network’, Meng et al. [19] focused on drug repositioning, the strategy that aims at repurposing existing drugs for new therapeutic purposes, which is also a pivotal approach to accelerate drug discovery. They introduced a novel deep-learning method called DRAGNN for drug repositioning. This approach uses a graph neural network and includes weighted local information augmentation. Specifically, the authors first incorporate a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. Their model demonstrates promising potential in predicting candidate drugs and drug combinations through molecular docking and network analysis experiments. In the article ‘Prediction of protein-ligand binding affinity via deep learning models’, Wang [20] provided us with a detailed overview of the current state-of-the-art computational methods, particularly deep learning-based models, that are being used to predict protein-ligand binding affinity. The author explained the fundamental principles of deep learning models in predicting the relationship between proteins and ligands. The author also examined the databases and input representations in this area. Based on the review, the author highlighted the potential challenges and future work required to predict the protein-ligand binding affinity accurately. The articles in this special issue showcase diverse advanced techniques for studying proteins and RNAs. We hope these methods deliver substantial value and offer profound insights to the protein and RNA research community. National Natural Science Foundation of China (grant no. 12175081); Fundamental Research Funds for the Central Universities (grant no. CCNU22QN004). No new data were generated or analyzed in support of this manuscript. Haoquan Liu is a PhD student in the College of Physical Science and Technology at Central China Normal University. His research is centered around network science and deep learning, particularly in the field of biomolecule mechanisms and interactions. Yunjie Zhao is a professor in the College of Physical Science and Technology at Central China Normal University. His research interests revolve around understanding the fundamental principles that underlie molecular structure and function. He focuses on designing biomolecules related to human diseases and developing computational tools to address challenges in health and technology.

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

Cyclin-dependent kinase (Cdk) proteins play crucial roles in the cell cycle progression and are thus attractive drug targets for therapy against such aberrant cell cycle processes as cancer. Since most of the available Cdk inhibitors target the highly conserved catalytic ATP pocket and their lack of specificity often lead to side effects, it is imperative to identify and characterize less conserved non-catalytic pockets capable of interfering with the kinase activity allosterically. However, a systematic analysis of these allosteric druggable pockets is still in its infancy. Here, we summarize the existing Cdk pockets and their selectivity. Then, we outline a network-based pocket prediction approach (NetPocket) and illustrate its utility for systematically identifying the allosteric druggable pockets with case studies. Finally, we discuss potential future directions and their challenges.

BACKGROUND: RNA regulates a variety of biological functions by interacting with other molecules. The ligand often binds in the RNA pocket to trigger structural changes or functions. Thus, it is essential to explore and visualize the RNA pocket to elucidate the structural and recognition mechanism for the RNA-ligand complex formation. RESULTS: In this work, we developed one user-friendly bioinformatics tool, RPocket. This database provides geometrical size, centroid, shape, secondary structure element for RNA pocket, RNA-ligand interaction information, and functional sites. We extracted 240 RNA pockets from 94 non-redundant RNA-ligand complex structures. We developed RPDescriptor to calculate the pocket geometrical property quantitatively. The geometrical information was then subjected to RNA-ligand binding analysis by incorporating the sequence, secondary structure, and geometrical combinations. This new approach takes advantage of both the atom-level precision of the structure and the nucleotide-level tertiary interactions. The results show that the higher-level topological pattern indeed improves the tertiary structure prediction. We also proposed a potential mechanism for RNA-ligand complex formation. The electrostatic interactions are responsible for long-range recognition, while the Van der Waals and hydrophobic contacts for short-range binding and optimization. These interaction pairs can be considered as distance constraints to guide complex structural modeling and drug design. CONCLUSION: RPocket database would facilitate RNA-ligand engineering to regulate the complex formation for biological or medical applications. RPocket is available at http://zhaoserver.com.cn/RPocket/RPocket.html .

BACKGROUND: It is widely believed that tertiary nucleotide-nucleotide interactions are essential in determining RNA structure and function. Currently, direct coupling analysis (DCA) infers nucleotide contacts in a sequence from its homologous sequence alignment across different species. DCA and similar approaches that use sequence information alone typically yield a low accuracy, especially when the available homologous sequences are limited. Therefore, new methods for RNA structural contact inference are desirable because even a single correctly predicted tertiary contact can potentially make the difference between a correct and incorrectly predicted structure. Here we present a new method DIRECT (Direct Information REweighted by Contact Templates) that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural features in contact inference. RESULTS: Benchmark tests demonstrate that DIRECT achieves better overall performance than DCA approaches. Compared to mfDCA and plmDCA, DIRECT produces a substantial increase of 41 and 18%, respectively, in accuracy on average for contact prediction. DIRECT improves predictions for long-range contacts and captures more tertiary structural features. CONCLUSIONS: We developed a hybrid approach that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural templates in contact inference. Our results demonstrate that DIRECT is able to improve the RNA contact prediction.

BACKGROUND: The kinase pocket structural information is important for drug discovery targeting cancer or other diseases. Although some kinase sequence, structure or drug databases have been developed, the databases cannot be directly used in the kinase drug study. Therefore, a comprehensive database of human kinase protein pockets is urgently needed to be developed. RESULTS: Here, we have developed HKPocket, a comprehensive Human Kinase Pocket database. This database provides sequence, structure, hydrophilic-hydrophobic, critical interactions, and druggability information including 1717 pockets from 255 kinases. We further divided these pockets into 91 pocket clusters using structural and position features in each kinase group. The pocket structural information would be useful for preliminary drug screening. Then, the potential drugs can be further selected and optimized by analyzing the sequence conservation, critical interactions, and hydrophobicity of identified drug pockets. HKPocket also provides online visualization and pse files of all identified pockets. CONCLUSION: The HKPocket database would be helpful for drug screening and optimization. Besides, drugs targeting the non-catalytic pockets would cause fewer side effects. HKPocket is available at http://zhaoserver.com.cn/HKPocket/HKPocket.html.

Motivation: Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions. Results: Current RNA binding site prediction algorithms produce many false positive nucleotides that are distance away from the binding sites. Here, we present a network approach, RBind, to predict the RNA binding sites. We benchmarked RBind in RNA-ligand and RNA-protein datasets. The average accuracy of 0.82 in RNA-ligand and 0.63 in RNA-protein testing showed that this network strategy has a reliable accuracy for binding sites prediction. Availability and implementation: The codes and datasets are available at https://zhaolab.com.cn/RBind. Supplementary information: Supplementary data are available at Bioinformatics online.

Different from the conventional operation mode in existing taxi dispatch systems, in this paper, we envision a new cyber-technology enabled taxi dispatch system which can efficiently provide vacant taxis with cruising route suggestions, not to respond to any specific pick-up request but instead, hoping to find prospective customers (such system is also complementary to the conventional operation mode). We address the Taxi Cruising Guidance (TCG) problem with the objective being to minimize the Global Vacant Rate (GVR), which is defined as the ratio of traveling miles with no passenger onboard, to the total traveling miles in a given time period. We propose a number of heuristic solutions and conduct comprehensive performance evaluations based on large-scale simulations. A case study is also presented by utilizing real traces collected from taxis in the city of Shanghai. As part of our research, we leverage a well-known microscopic traffic simulator (called TRANSIMS) to demonstrate that the application of TCG is also beneficial to traffic management.

We consider a promising application in Vehicular Cyber-Physical Systems (VCPS) called On-road Ad Delivery (OAD), where targeted advertisements are delivered via roadside APs to attract commuters to nearby shops. Different from most existing works on VANETs which only focused on a single technical area, this work on OAD involves technical elements from human factors, cyber systems and transportation systems since a commuter's shopping decision depends on e.g. the attractiveness of the ads, the induced detour, and traffic conditions on different routes. In this paper, we address a new optimization problem in OAD whose goal is to schedule ad messages and allocate a limited amount of AP bandwidth so as to maximize the system-wide performance in terms of total realized utilities (TRU) of the delivered ads. A number of efficient heuristics are proposed to deal with ad message scheduling and AP bandwidth allocation. Besides largescale simulations, we also present a case study in a more realistic scenario utilizing real traces collected from taxis in the city of Shanghai. In addition, we use a commercial traffic simulator (PARAMICS) to show that our proposed solutions are also useful for traffic management in terms of balancing vehicular traffic and alleviating congestion.

The connected vehicle research program is a multimodal research initiative in the U.S. that envisions a fully connected transportation system with wireless communications linking vehicles, the infrastructure, and handheld smart devices. This paper designs and evaluates a reservation-based approach to intersection control that is designed to take full advantage of the unprecedented connectivity that the connected vehicle initiative promises to provide. The control approach, which is referred to herein as the “intelligent intersection” approach, builds on the previous work by Dresner and Stone by introducing new features to better account for several aspects of the real-world driving environment. To design and evaluate the “intelligent intersection,” a novel simulation test bed for connected vehicle applications is developed. The test bed integrates a microscopic traffic simulator with a network simulator and an emission analyzer. Using the integrated simulator, the mobility and environmental benefits of the intelligent intersection approach, compared with those of traditional control methods, are evaluated on two case studies: 1) an isolated intersection and 2) a real-world transportation network with multiple intersections. Results show that the proposed control approach offers significant mobility and environmental benefits. For example, for the second test case and using observed traffic volumes, the intelligent intersection reduced the average vehicle delay by 85%, fuel consumption by 50%, and emissions by 39%-50%. The study also demonstrates the utility of using the simulator test bed in the design and evaluation of connected vehicle applications.