Multidimensional IRT for forced choice tests: A literature review.

The Multidimensional Forced Choice (MFC) test is frequently utilized in non-cognitive evaluations because of its effectiveness in reducing response bias commonly associated with the conventional Likert scale. Nonetheless, it is critical to recognize that the MFC test generates ipsative data, a type of measurement that has been criticized due to its limited applicability for comparing individuals. Multidimensional item response theory (MIRT) models have recently sparked renewed interest among academics and professionals. This is largely due to the development of several models that make it easier to collect normative data from forced-choice tests. The paper introduces a modeling framework made up of three key components: response format, measurement model, and decision theory. Under this paradigm, four IRT models were chosen as examples. Following that, a comprehensive study is carried out to compare and characterize the parameter estimation techniques used in MFC-IRT models. This work then examines empirical research on the concept by analyzing three distinct domains: parameter invariance testing, computerized adaptive testing (CAT), and validity investigation. Finally, it is recommended that future research initiatives follow four distinct paths: modeling, parameter invariance testing, forced-choice CAT, and validity studies.

A 2PLM-RANK multidimensional forced-choice model and its fast estimation algorithm.

High-stakes non-cognitive tests frequently employ forced-choice (FC) scales to deter faking. To mitigate the issue of score ipsativity derived, many scoring models have been devised. Among them, the multi-unidimensional pairwise preference (MUPP) framework is a highly flexible and commonly used framework. However, the original MUPP model was developed for unfolding response process and can only handle paired comparisons. The present study proposes the 2PLM-RANK as a generalization of the MUPP model to accommodate dominance RANK format response. In addition, an improved stochastic EM (iStEM) algorithm is devised for more stable and efficient parameter estimation. Simulation results generally supported the efficiency and utility of the new algorithm in estimating the 2PLM-RANK when applied to both triplets and tetrads across various conditions. An empirical illustration with responses to a 24-dimensional personality test further supported the practicality of the proposed model. To further aid in the application of the new model, a user-friendly R package is also provided.

Mechanistic study of the transmission pattern of the SARS-CoV-2 omicron variant.

The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) characterized by 30 mutations in its spike protein, has rapidly spread worldwide since November 2021, significantly exacerbating the ongoing COVID-19 pandemic. In order to investigate the relationship between these mutations and the variant's high transmissibility, we conducted a systematic analysis of the mutational effect on spike-angiotensin-converting enzyme-2 (ACE2) interactions and explored the structural/energy correlation of key mutations, utilizing a reliable coarse-grained model. Our study extended beyond the receptor-binding domain (RBD) of spike trimer through comprehensive modeling of the full-length spike trimer rather than just the RBD. Our free-energy calculation revealed that the enhanced binding affinity between the spike protein and the ACE2 receptor is correlated with the increased structural stability of the isolated spike protein, thus explaining the omicron variant's heightened transmissibility. The conclusion was supported by our experimental analyses involving the expression and purification of the full-length spike trimer. Furthermore, the energy decomposition analysis established those electrostatic interactions make major contributions to this effect. We categorized the mutations into four groups and established an analytical framework that can be employed in studying future mutations. Additionally, our calculations rationalized the reduced affinity of the omicron variant towards most available therapeutic neutralizing antibodies, when compared with the wild type. By providing concrete experimental data and offering a solid explanation, this study contributes to a better understanding of the relationship between theories and observations and lays the foundation for future investigations.

In Silico Optimization of SARS-CoV-2 Spike Specific Nanobodies.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide, caused a global pandemic, and killed millions of people. The spike protein embedded in the viral membrane is essential for recognizing human receptors and invading host cells. Many nanobodies have been designed to block the interaction between spike and other proteins. However, the constantly emerging viral variants limit the effectiveness of these therapeutic nanobodies. Therefore, it is necessary to find a prospective antibody designing and optimization approach to deal with existing or future viral variants. METHODS: We attempted to optimize nanobody sequences based on the understanding of molecular details by using computational approaches. First, we employed a coarse-grained (CG) model to learn the energetic mechanism of the spike protein activation. Next, we analyzed the binding modes of several representative nanobodies with the spike protein and identified the key residues on their interfaces. Then, we performed saturated mutagenesis of these key residue sites and employed the CG model to calculate the binding energies. RESULTS: Based on analysis of the folding energy of the angiotensin-converting enzyme 2 (ACE2) -spike complex, we constructed a detailed free energy profile of the activation process of the spike protein which provided a clear mechanistic explanation. In addition, by analyzing the results of binding free energy changes following mutations, we determined how the mutations can improve the complementarity with the nanobodies on spike protein. Then we chose 7KSG nanobody as a template for further optimization and designed four potent nanobodies. Finally, based on the results of the single-site saturated mutagenesis in complementarity determining regions (CDRs), combinations of mutations were performed. We designed four novel, potent nanobodies, all exhibiting higher binding affinity to the spike protein than the original ones. CONCLUSIONS: These results provide a molecular basis for the interactions between spike protein and antibodies and promote the development of new specific neutralizing nanobodies.

Exploring the Phospholipid Transport Mechanism of ATP8A1-CDC50.

P4-ATPase translocates lipids from the exoplasmic to the cytosolic plasma membrane leaflet to maintain lipid asymmetry distribution in eukaryotic cells. P4-ATPase is associated with severe neurodegenerative and metabolic diseases such as neurological and motor disorders. Thus, it is important to understand its transport mechanism. However, even with progress in X-ray diffraction and cryo-electron microscopy techniques, it is difficult to obtain the dynamic information of the phospholipid transport process in detail. There are still some problems required to be resolved: (1) when does the lipid transport happen? (2) How do the key residues on the transmembrane helices contribute to the free energy of important states? In this work, we explore the phospholipid transport mechanism using a coarse-grained model and binding free energy calculations. We obtained the free energy landscape by coupling the protein conformational changes and the phospholipid transport event, taking ATP8A1-CDC50 (the typical subtype of P4-ATPase) as the research object. According to the results, we found that the phospholipid would bind to the ATP8A1-CDC50 at the early stage when ATP8A1-CDC50 changes from E2P to E2Pi-PL state. We also found that the electrostatic effects play crucial roles in the phospholipid transport process. The information obtained from this work could help us in designing novel drugs for P-type flippase disorders.

Mechanism Study of Proteins under Membrane Environment.

Membrane proteins play crucial roles in various physiological processes, including molecule transport across membranes, cell communication, and signal transduction. Approximately 60% of known drug targets are membrane proteins. There is a significant need to deeply understand the working mechanism of membrane proteins in detail, which is a challenging work due to the lack of available membrane structures and their large spatial scale. Membrane proteins carry out vital physiological functions through conformational changes. In the current study, we utilized a coarse-grained (CG) model to investigate three representative membrane protein systems: the TMEM16A channel, the family C GPCRs mGlu2 receptor, and the P4-ATPase phospholipid transporter. We constructed the reaction pathway of conformational changes between the two-end structures. Energy profiles and energy barriers were calculated. These data could provide reasonable explanations for TMEM16A activation, the mGlu2 receptor activation process, and P4-ATPase phospholipid transport. Although they all belong to the members of membrane proteins, they behave differently in terms of energy. Our work investigated the working mechanism of membrane proteins and could give novel insights into other membrane protein systems of interest.

Application of Coarse-Grained (CG) Models to Explore Conformational Pathway of Large-Scale Protein Machines.

Protein machines are clusters of protein assemblies that function in order to control the transfer of matter and energy in cells. For a specific protein machine, its working mechanisms are not only determined by the static crystal structures, but also related to the conformational transition dynamics and the corresponding energy profiles. With the rapid development of crystallographic techniques, the spatial scale of resolved structures is reaching up to thousands of residues, and the concomitant conformational changes become more and more complicated, posing a great challenge for computational biology research. Previously, a coarse-grained (CG) model aiming at conformational free energy evaluation was developed and showed excellent ability to reproduce the energy profiles by accurate electrostatic interaction calculations. In this study, we extended the application of the CG model to a series of large-scale protein machine systems. The spike protein trimer of SARS-CoV-2, ATP citrate lyase (ACLY) tetramer, and P4-ATPases systems were carefully studied and discussed as examples. It is indicated that the CG model is effective to depict the energy profiles of the conformational pathway between two endpoint structures, especially for large-scale systems. Both the energy change and energy barrier between endpoint structures provide reasonable mechanism explanations for the associated biological processes, including the opening of receptor binding domain (RBD) of spike protein, the phospholipid transportation of P4-ATPase, and the loop translocation of ACLY. Taken together, the CG model provides a suitable alternative in mechanistic studies related to conformational change in large-scale protein machines.

A systematic study on the binding affinity of SARS-CoV-2 spike protein to antibodies.

The COVID-19 pandemic has caused a worldwide health crisis and economic recession. Effective prevention and treatment methods are urgently required to control the pandemic. However, the emergence of novel SARS-CoV-2 variants challenges the effectiveness of currently available vaccines and therapeutic antibodies. In this study, through the assessment of binding free energies, we analyzed the mutational effects on the binding affinity of the coronavirus spike protein to neutralizing antibodies, patient-derived antibodies, and artificially designed antibody mimics. We designed a scoring method to assess the immune evasion ability of viral variants. We also evaluated the differences between several targeting sites on the spike protein of antibodies. The results presented herein might prove helpful in the development of more effective therapies in the future.

Predicting Mutational Effects on Receptor Binding of the Spike Protein of SARS-CoV-2 Variants.

The pandemic caused by SARS-CoV-2 has cost millions of lives and tremendous social/financial loss. The virus continues to evolve and mutate. In particular, the recently emerged "UK", "South Africa", and Delta variants show higher infectivity and spreading speed. Thus, the relationship between the mutations of certain amino acids and the spreading speed of the virus is a problem of great importance. In this respect, understanding the mutational mechanism is crucial for surveillance and prediction of future mutations as well as antibody/vaccine development. In this work, we used a coarse-grained model (that was used previously in predicting the importance of mutations of N501) to calculate the free energy change of various types of single-site or combined-site mutations. This was done for the UK, South Africa, and Delta mutants. We investigated the underlying mechanisms of the binding affinity changes for mutations at different spike protein domains of SARS-CoV-2 and provided the energy basis for the resistance of the E484 mutant to the antibody m396. Other potential mutation sites were also predicted. Furthermore, the in silico predictions were assessed by functional experiments. The results establish that the faster spreading of recently observed mutants is strongly correlated with the binding-affinity enhancement between virus and human receptor as well as with the reduction of the binding to the m396 antibody. Significantly, the current approach offers a way to predict new variants and to assess the effectiveness of different antibodies toward such variants.

MethHC 2.0: information repository of DNA methylation and gene expression in human cancer.

DNA methylation is an important epigenetic regulator in gene expression and has several roles in cancer and disease progression. MethHC version 2.0 (MethHC 2.0) is an integrated and web-based resource focusing on the aberrant methylomes of human diseases, specifically cancer. This paper presents an updated implementation of MethHC 2.0 by incorporating additional DNA methylomes and transcriptomes from several public repositories, including 33 human cancers, over 50 118 microarray and RNA sequencing data from TCGA and GEO, and accumulating up to 3586 manually curated data from >7000 collected published literature with experimental evidence. MethHC 2.0 has also been equipped with enhanced data annotation functionality and a user-friendly web interface for data presentation, search, and visualization. Provided features include clinical-pathological data, mutation and copy number variation, multiplicity of information (gene regions, enhancer regions, and CGI regions), and circulating tumor DNA methylation profiles, available for research such as biomarker panel design, cancer comparison, diagnosis, prognosis, therapy study and identifying potential epigenetic biomarkers. MethHC 2.0 is now available at http://awi.cuhk.edu.cn/∼MethHC.