Consensus Structure Research Articles

Multiview Robust Graph-Based Clustering for Cancer Subtype Identification.

Cancer subtype identification is to classify cancer into groups according to their molecular characteristics and clinical manifestations and is the basis for more personalized diagnosis and therapy. Public datasets such as The Cancer Genome Atlas (TCGA) have collected a massive number of multi-omics data. The accumulation of these datasets provides unprecedented opportunities to study the mechanism of cancers and further identify cancer subtypes at a comprehensive level. In this paper, we propose a multi-view robust graph-based clustering (MRGC) method to effectively identify cancer subtypes. Our method first learns robust latent representations from the raw omics data to alleviate the influences of the noise, where a set of similarity matrices are then adaptively learned based on these new representations. Finally, a global similarity graph is obtained by exploiting the consensus structure from the graphs. As a result, the three parts in our method can reinforce each other in a mutual iterative manner. We conduct extensive experiments on both generic machine learning datasets and cancer datasets. The experimental results confirm that our model can achieve satisfactory clustering performance compared to several state-of-the-art approaches. Moreover, we convey the practicability of MRGC by carrying out a case study on hepatocellular carcinoma.

Dynamic community detection considering daily rhythms of human mobility

Detecting human mobility community structures is an important means to reveal urban spatial structures. Due to the circadian clock, human mobility has a daily rhythm, representing dynamic interactions among urban functional areas at different times of the day. However, few community detection methods have been developed by explicitly considering such daily rhythms of human mobility. To fill the gap, this study proposes a novel method for detecting dynamic community structures derived from the daily rhythms of human mobility. The proposed method employs a consensus clustering technique to detect consensus structures of dynamic communities throughout the day. The time series characteristics of the detected dynamic communities are further introduced. The hierarchical clustering technique is used to uncover evolving patterns of detected dynamic communities. A case study was carried out using taxi trajectories in New York City to verify the proposed method. Results indicated that the proposed method could well detect a robust structure of dynamic communities even when human mobility patterns changed dramatically at certain time periods. Results also suggested that the proposed method could identify underlying evolving patterns of detected dynamic communities.

Self-Renewal Consortium Blockchain Based on Proof of Rest and Strong Smart Contracts

Focusing on the business alliance scenario in blockchains, this paper proposes a new consensus mechanism named proof of rest (PoR) and strong smart contracts. The block structure and logic of PoR consensus are described. And a consortium blockchain system supporting strong smart contracts is designed. We modify the difficulty value algorithm based on proof of work (PoW) and add adjustable parameters. The longer a node rests after creating a block, the less difficult it is to create another new block, hence the term PoR. The penalty for slack nodes, the joining and quitting of nodes, and the adjustment of the expected block creation time can all be accomplished using the strong smart contracts, so the consortium blockchain can realize self-renewal.

A Multiview Clustering Method With Low-Rank and Sparsity Constraints for Cancer Subtyping.

Multiomics data clustering is one of the major challenges in the field of precision medicine. Integration of multiomics data for cancer subtyping can improve the understanding on cancer and reveal systems-level insights. How to integrate multiomics data for accurate cancer subtyping is an interesting and challenging research problem. To capture the global and the local structure of omics data, a novel framework for integrating multiomics data is proposed for cancer subtyping. Multiview clustering with low-rank and sparsity constraints (MVCLRS) can measure the local similarities of samples in each omics data and obtain global consensus structures by integrating the multiomics data. The main insight provided by MVCLRS is that low-rank sparse subspace clustering for the construction of an affinity matrix can best capture the local similarities in omics data. Extensive testing is conducted on 10 real world cancer datasets with multiomics from The Cancer Genome Atlas. Compared with 10 state-of-the-art multiomics clustering algorithms, the MVCLRS performs better in the 10 cancer datasets by providing its clustering results with at least one enriched clinical label in nine of ten cancer subtypes, the most of any method.

Characterization of chicken p53 transcriptional function via parallel genome-wide chromatin occupancy and gene expression analysis

The tumor suppressor p53, which acts primarily as a transcription factor, can regulate infections from various viruses in chickens. However, the underlying mechanisms of the antiviral functions of chicken p53 (chp53) remain unclear due to the lack of detailed information on its transcriptional regulation. Here, to gain comprehensive insights into chp53 transcriptional regulatory function in a global and unbiased manner, we determined the genome-wide chromatin occupancy of chp53 by chromatin immunoprecipitation, which was followed by sequencing and chp53-mediated gene expression profile by RNA sequencing using chemically immortalized leghorn male hepatoma (LMH) cells with ectopic expression of chp53 as the model. The integrated parallel genome-wide chromatin occupancy and gene expression analysis characterized chp53 chromatin occupancy and identified 754 direct target genes of chp53. Furthermore, functional annotation and cross-species comparative biological analyses revealed the conserved key biological functions and DNA binding motifs of p53 between chickens and humans, which may be due to the consensus amino acid sequence and structure of p53 DNA-binding domains. The present study, to our knowledge, provides the first comprehensive characterization of the chp53 transcriptional regulatory network, and can possibly help to improve our understanding of p53 transcriptional regulatory mechanisms and their antiviral functions in chickens.

Pseudolabel‐guided multiview consensus graph learning for semisupervised classification

Semisupervised multiview learning gains extensive research attention due to its strong capability to utilize the heterogeneous features and the label information of a few labeled samples. However, the supervision information is not well utilized in the process of exploring the consensus structure of the multiview data. In this paper, we propose a novel unified pseudolabel-guided multiview consensus (PMvC) learning framework for the semisupervised classification problem, which learns the consensus structure of multiview data by fully exploiting the supervised information of labeled samples. Specifically, PMvC first assigns multiple pseudolabels to the unlabeled samples by selecting the nearest labeled sample in each view separately, and then labels the part of unlabeled samples by selecting the pseudolabel that agrees across all views. By doing so, the high-confident pseudolabeled samples can be selected to enlarge the labeled sample pool and the supervision information can be exploited further in the learning process. In addition, to capture the consensus structure of the multiview data, PMvC learns a consensus graph from the view-specific self-representation graph guided by enhanced supervision information, which better preserves the manifold structure of samples. Meanwhile, the label information is also propagated from the labeled samples to the unlabeled samples by the learned consensus graph simultaneously. Accordingly, an effective optimization algorithm is derived to find the optimal solution for PMvC. Extensive experiment results on several real-world data sets demonstrate the feasibility and superiority of PMvC. The source code of PMvC is available at https://github.com/justcallmewilliam/PMvC.

Treatment Planning for Cardiac Radioablation: Multicenter Multiplatform Benchmarking for the RAdiosurgery for VENtricular TAchycardia (RAVENTA) Trial

Cardiac radioablation is a novel treatment option for patients with refractory ventricular tachycardia unsuitable for catheter ablation. The quality of treatment planning depends on dose specifications, platform capabilities, and experience of the treating staff. To harmonize the treatment planning, benchmarking of this process is necessary for multicenter clinical studies such as the RAdiosurgery for VENtricular TAchycardia trial. Planning computed tomography data and consensus structures from 3 patients were sent to 5 academic centers for independent plan development using a variety of platforms and techniques with the RAdiosurgery for VENtricular TAchycardia study protocol serving as guideline. Three-dimensional dose distributions and treatment plan details were collected and analyzed. In addition, an objective relative plan quality ranking system for ventricular tachycardia treatments was established. For each case, 3 coplanar volumetric modulated arc (VMAT) plans for C-arm linear accelerators (LINAC) and 3 noncoplanar treatment plans for robotic arm LINAC were generated. All plans were suitable for clinical applications with minor deviations from study guidelines in most centers. Eleven of 18 treatment plans showed maximal one minor deviation each for target and cardiac substructures. However, dose-volume histograms showed substantial differences: in one case, the planning target volume ≥30 Gy ranged from 0.0% to 79.9% and the ramus interventricularis anterior V14Gy ranged from 4.0% to 45.4%. Overall, the VMAT plans had steeper dose gradients in the high-dose region, while the plans for the robotic arm LINAC had smaller low-dose regions. Thereby, VMAT plans required only about half as many monitor units, resulting in shorter delivery times, possibly an important factor in treatment outcome. Cardiac radioablation is feasible with robotic arm and C-arm LINAC systems with comparable plan quality. Although cross-center training and best practice guidelines have been provided, further recommendations, especially for cardiac substructures, and ranking of dose guidelines will be helpful to optimize cardiac radioablation outcomes.

Distributed Kalman Consensus Filter for Estimation With Moving Targets.

Consensus-based distributed Kalman filters for estimation with targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. In this article, we address these issues by designing a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation in continuous time. A new moving target information-flow topology for the measurement of targets is developed based on the sensors' sensing ranges, targets' random mobility, and local information-weighted neighbors. Novel necessary and sufficient conditions about the convergence of the proposed DKCF are developed. Under these conditions, the estimates of all sensors converge to the consensus values. Simulation and comparative studies show the effectiveness and the superiority of this new DKCF.

KnotAli: informed energy minimization through the use of evolutionary information

BackgroundImproving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a family to predict the structure. However, their prediction is limited to the consensus structure, and by the quality of the alignment. Minimum free energy (MFE) based methods, on the other hand, do not rely on familial information and can predict structures of novel RNA molecules. Their prediction normally suffers from inaccuracies due to their underlying energy parameters.ResultsWe present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes a multiple RNA sequence alignment as input and uses covariation and thermodynamic energy minimization to predict possibly pseudoknotted secondary structures for each individual sequence in the alignment. We compared KnotAli’s performance to that of three other alignment-based programs, two that can handle pseudoknotted structures and one control, on a large data set of 3034 RNA sequences with varying lengths and levels of sequence conservation from 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT).ConclusionsWe found KnotAli’s performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. While both KnotAli and Cacofold use background noise correction strategies, we found KnotAli’s predictions to be less dependent on the alignment quality. KnotAli can be found online at the Zenodo image: https://doi.org/10.5281/zenodo.5794719

Insights into the secondary and tertiary structure of the Bovine Viral Diarrhea Virus Internal Ribosome Entry Site

ABSTRACT The internal ribosome entry site (IRES) RNA of bovine viral diarrhoea virus (BVDV), an economically significant Pestivirus, is required for the cap-independent translation of viral genomic RNA. Thus, it is essential for viral replication and pathogenesis. We applied a combination of high-throughput biochemical RNA structure probing (SHAPE-MaP) and in silico modelling approaches to gain insight into the secondary and tertiary structures of BVDV IRES RNA. Our study demonstrated that BVDV IRES RNA in solution forms a modular architecture composed of three distinct structural domains (I–III). Two regions within domain III are represented in tertiary interactions to form an H-type pseudoknot. Computational modelling of the pseudoknot motif provided a fine-grained picture of the tertiary structure and local arrangement of helices in the BVDV IRES. Furthermore, comparative genomics and consensus structure predictions revealed that the pseudoknot is evolutionarily conserved among many Pestivirus species. These studies provide detailed insight into the structural arrangement of BVDV IRES RNA H-type pseudoknot and encompassing motifs that likely contribute to the optimal functionality of viral cap-independent translation element.

Cognitive multi-modal consistent hashing with flexible semantic transformation

Multi-modal hashing can encode the large-scale social geo-media multimedia data from multiple sources into a common discrete hash space, in which the heterogeneous correlations from multiple modalities could be well explored and preserved into the objective semantic-consistent hash codes. The current researches on multi-modal hashing mainly focus on performing common data reconstruction, but they fail to effectively distill the intrinsic and consensus structures of multi-modal data and fully exploit the inherent semantic knowledge to capture semantic-consistent information across multiple modalities, leading to unsatisfactory retrieval performance. To facilitate this problem and develop an efficient multi-modal geographical retrieval method, in this article, we propose a discriminative multi-modal hashing framework named Cognitive Multi-modal Consistent Hashing (CMCH), which can progressively pursue the structure consensus over heterogeneous multi-modal data and simultaneously explore the informative transformed semantics. Specifically, we construct a parameter-free collaborative multi-modal fusion module to incorporate and excavate the underlying common components from multi-source data. Particularly, our formulation seeks for a joint multi-modal compatibility among multiple modalities under a self-adaptive weighting manner, which can take full advantages of their complementary properties. Moreover, a cognitive self-paced learning policy is further leveraged to conduct progressive feature aggregation, which can coalesce multi-modal data onto the established common latent space in a curriculum learning mode. Furthermore, deep semantic transform learning is elaborated to generate flexible semantics for interactively guiding collaborative hash codes learning. An efficient discrete learning algorithm is devised to address the resulting optimization problem, which obtains stable solutions when dealing with large-scale multi-modal retrieval tasks. Sufficient experiments performed on four large-scale multi-modal datasets demonstrate the encouraging performance of the proposed CMCH method in comparison with the state-of-the-arts over multi-modal information retrieval and computational efficiency. The source codes of this work could be available at https://github.com/JunfengAn1998a/CMCH .

Cryo-EM structure of the dual incretin receptor agonist, peptide-19, in complex with the glucagon-like peptide-1 receptor

Dual agonists that can activate both the glucagon-like peptide-1 receptor (GLP-1R) and the gastric inhibitory polypeptide receptor (GIPR) have demonstrated high efficacy for the treatment of metabolic disease. Peptide-19 is a prototypical dual agonist that has high potency at both GLP-1R and GIPR but has a distinct signalling profile relative to the native peptides at the cognate receptors. In this study, we solved the structure of peptide-19 bound to the GLP-1R in complex with Gs protein, and compared the structure and dynamics of this complex to that of published structures of GLP-1R:Gs in complex with other receptor agonists. Unlike other peptide-bound receptor complexes, peptide-19:GLP-1R:Gs demonstrated a more open binding pocket where transmembrane domain (TM) 6, TM7 and the interconnecting extracellular loop 3 (ECL3) were located away from the peptide, with no interactions between peptide-19 and TM6/ECL3. Analysis of conformational variance of the complex revealed that peptide-19 was highly dynamic and underwent binding and unbinding motions facilitated by the more open TM binding pocket. Both the consensus structure of the GLP-1R complex with peptide-19 and the dynamics of this complex were distinct from previously described GLP-1R structures providing unique insights into the mode of GLP-1R activation by this dual agonist.

Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.

Comparative genomics provides structural and functional insights into Bacteroides RNA biology.

Bacteria employ noncoding RNA molecules for a wide range of biological processes, including scaffolding large molecular complexes, catalyzing chemical reactions, defending against phages, and controlling gene expression. Secondary structures, binding partners, and molecular mechanisms have been determined for numerous small noncoding RNAs (sRNAs) in model aerobic bacteria. However, technical hurdles have largely prevented analogous analyses in the anaerobic gut microbiota. While experimental techniques are being developed to investigate the sRNAs of gut commensals, computational tools and comparative genomics can provide immediate functional insight. Here, using Bacteroides thetaiotaomicron as a representative microbiota member, we illustrate how comparative genomics improves our understanding of RNA biology in an understudied gut bacterium. We investigate putative RNA-binding proteins and predict a Bacteroides cold-shock protein homolog to have an RNA-related function. We apply an in silico protocol incorporating both sequence and structural analysis to determine the consensus structures and conservation of nine Bacteroides noncoding RNA families. Using structure probing, we validate and refine these predictions and deposit them in the Rfam database. Through synteny analyses, we illustrate how genomic coconservation can serve as a predictor of sRNA function. Altogether, this work showcases the power of RNA informatics for investigating the RNA biology of anaerobic microbiota members.

Does risk matter for executive compensation?

PurposeThe purpose of this study is to examine whether chief executive officer (CEOs) are paid for the systematic and/or unsystematic risks and whether there is any optimum risk premium level in the executive pay.Design/methodology/approachFirm and year fixed effect panel data regression was used to estimate the relationship between total CEO compensation and systematic (market) and unsystematic (firm) risks.FindingsThere is no nexus between CEO pay and unsystematic (diversifiable) risk; however, the association between CEO compensation and systematic (undiversifiable) risk is positively significant in line with agency theory. Moreover, it is revealed that this positive relationship has an optimum point (curvilinear).Research limitations/implicationsThis paper contributes to the controversial argument in the literature by investigating the situation in the Swiss market. Switzerland is an exemplary country because of its direct democracy (consensus) structure for executive pay. This study is limited by the fact that only total CEO compensation is analyzed.Practical implicationsAs a practical implication, it is shown that after the optimal point, the higher compensation does not motivate the CEOs to take higher risks and does not provide the organizations with any additional benefit.Originality/valueThe finding of this study supports agency theory’s risk premium assumption and provides additional evidence to the contradictory results in the literature with a new country setting that has paramount importance in executive compensation phenomena. It is a comparative finding with prior literature also outlines the future research area in the risk and compensation literature.

The 3D Organization of Chromatin Colors in Mammalian Nuclei.

While many computational methods have been proposed for 3D chromosome reconstruction from chromosomal contact maps, these methods are rarely used for the interpretation of such experimental data, in particular Hi-C data. We posit that this is due to the lack of an easy-to-use implementation of the proposed algorithms, as well as to the important computational cost of most methods. We here give a detailed implementation of the fast ShRec3D algorithm. We provide a tutorial that will enable the reader to reconstruct 3D consensus structures for human chromosomes and to decorate these structures with chromatin epigenetic states. We use this methodology to show that the bivalent chromatin, including Polycomb-rich domains, is spatially segregated and located in between the active and the quiescent chromatin compartments.

Structural discrimination analysis for constraint selection in protein modeling.

Protein structure modeling can be improved by the use of distance constraints between amino acid residues, provided such data reflects-at least partially-the native tertiary structure of the target system. In fact, only a small subset of the native contact map is necessary to successfully drive the model conformational search, so one important goal is to obtain the set of constraints with the highest true-positive rate, lowest redundancy and greatest amount of information. In this work, we introduce a constraint evaluation and selection method based on the point-biserial correlation coefficient, which utilizes structural information from an ensemble of models to indirectly measure the power of each constraint in biasing the conformational search toward consensus structures. Residue contact maps obtained by direct coupling analysis are systematically improved by means of discriminant analysis, reaching in some cases accuracies often seen only in modern deep-learning-based approaches. When combined with an iterative modeling workflow, the proposed constraint classification optimizes the selection of the constraint set and maximizes the probability of obtaining successful models. The use of discriminant analysis for the valorization of the information of constraint datasets is a general concept with possible applications to other constraint types and modeling problems. MSA for the targets in this work is available on https://github.com/m3g/2021_Bottino_Biserial. Modeling data supporting the findings of this study was generated at the Center for Computing in Engineering and Sciences, and is available from the corresponding author LM on request. Supplementary data are available at Bioinformatics online.

Specificity of mRNA Folding and Its Association with Evolutionarily Adaptive mRNA Secondary Structures

The secondary structure is a fundamental feature of both non-coding RNAs (ncRNAs) and messenger RNAs (mRNAs). However, our understanding of the secondary structures of mRNAs, especially those of the coding regions, remains elusive, likely due to translation and the lack of RNA-binding proteins that sustain the consensus structure like those binding to ncRNAs. Indeed, mRNAs have recently been found to adopt diverse alternative structures, but the overall functional significance remains untested. We hereby approach this problem by estimating the folding specificity, i.e., the probability that a fragment of an mRNA folds back to the same partner once refolded. We show that the folding specificity of mRNAs is lower than that of ncRNAs and exhibits moderate evolutionary conservation. Notably, we find that specific rather than alternative folding is likely evolutionarily adaptive since specific folding is frequently associated with functionally important genes or sites within a gene. Additional analysis in combination with ribosome density suggests the ability to modulate ribosome movement as one potential functional advantage provided by specific folding. Our findings reveal a novel facet of the RNA structurome with important functional and evolutionary implications and indicate a potential method for distinguishing the mRNA secondary structures maintained by natural selection from molecular noise.

Interdisciplinary Clinical Target Volume Generation for Cardiac Radioablation: Multicenter Benchmarking for the RAdiosurgery for VENtricular TAchycardia (RAVENTA) Trial

Cardiac radioablation is a novel treatment option for therapy-refractory ventricular tachycardia (VT) ineligible for catheter ablation. Three-dimensional clinical target volume (CTV) definition is a key step, and this complex interdisciplinary procedure includes VT-substrate identification based on electroanatomical mapping (EAM) and its transfer to the planning computed tomography (PCT). Benchmarking of this process is necessary for multicenter clinical studies such as the RAVENTA trial. For benchmarking of the RAVENTA trial, patient data (epicrisis, electrocardiogram, high-resolution EAM, contrast-enhanced cardiac computed tomography, PCT) of 3 cases were sent to 5 university centers for independent CTV generation, subsequent structure analysis, and consensus finding. VT substrates were first defined on multiple EAM screenshots/videos and manually transferred to the PCT. The generated structure characteristics were then independently analyzed (volume, localization, surface distance and conformity). After subsequent discussion, consensus structures were defined. VT substrate on the EAM showed visible variability in extent and localization for cases 1 and 2 and only minor variability for case 3. CTVs ranged from 6.7 to 22.9 cm3, 5.9 to 79.9 cm3, and 9.4 to 34.3 cm3; surface area varied from 1087 to 3285 mm2, 1077 to 9500 mm2, and 1620 to 4179 mm2, with a Hausdorff-distance of 15.7 to 39.5 mm, 23.1 to 43.5 mm, and 15.9 to 43.9 mm for cases 1 to 3, respectively. The absolute 3-dimensional center-of-mass difference was 5.8 to 28.0 mm, 8.4 to 26 mm, and 3.8 to 35.1 mm for cases 1 to 3, respectively. The entire process resulted in CTV structures with a conformity index of 0.2 to 0.83, 0.02 to 0.85, and 0.02 to 0.88 (ideal 1) with the consensus CTV as reference. Multicenter efficacy endpoint assessment of cardiac radioablation for therapy-refractory VT requires consistent CTV transfer methods from the EAM to the PCT. VT substrate definition and CTVs were comparable with current clinical practice. Remarkable differences regarding the degree of agreement of the CTV definition on the EAM and the PCT were noted, indicating a loss of agreement during the transfer process between EAM and PCT. Cardiac radioablation should be performed under well-defined protocols and in clinical trials with benchmarking and consensus forming.

Introduction and Fundamentals of Human Islet Amyloid Polypeptide Inhibitors.

Type 2 diabetes (T2D) is a common protein misfolding disease (PMD), and its pathogenesis is considered to be tightly associated with the aggregation of the disease-causative hIAPP (or amylin). Numerous studies have shown a possible pathological link between hIAPP aggregation and β-cell death; thus, different-level strategies from basic research to clinical bench applications have been applied to discover and design different types of inhibitors for preventing hIAPP aggregation and toxicity. This review surveys recent and important advances of hIAPP aggregation inhibitors in the context of amyloid aggregation, toxicity, and inhibition. Many hIAPP inhibitors have been explored to exert different inhibitory functions on hIAPP aggregation via different pathways. A further overview of molecular simulations of inhibitor-hIAPP systems highlights some consensus binding sequences and structures of hIAPP for different inhibitors, which provide molecular insights into well-defined binding targets and binding-induced inhibition mechanisms for structural-based design of hIAPP inhibitors. In a broader view, while anti-aggregation inhibitors hold substantial promise in the prevention of PMDs, many challenges still remain and need to be addressed for advancing our fundamental understanding of amyloid aggregation and practical design of clinically relevant inhibitors to treat PMDs.