GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation.

Exploring the potential energy surface (PES) of molecular systems is important for comprehending their complex behaviors, particularly through the identification of various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational resources. Our study introduces the gradient-based navigation (GradNav) algorithm, which accelerates the exploration of the energy surface and enables proper reconstruction of the PES. This algorithm employs a strategy of initiating short simulation runs from updated starting points derived from prior observations to effectively navigate across potential barriers and explore new regions. To evaluate GradNav's performance, we introduce two metrics: the deepest well escape frame (DWEF) and the search success initialization ratio (SSIR). Through applications on Langevin dynamics within Müller-type PESs and molecular dynamics simulations of the Fs-peptide protein, these metrics demonstrate GradNav's enhanced ability to escape deep energy wells and its reduced reliance on initial conditions, as denoted by the reduced DWEF values and increased SSIR values, respectively. Consequently, this improved exploration capability enables more precise energy estimations from simulation trajectories.

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models.

With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction.

Recent advances in language models have enabled the protein modeling community with a powerful tool that uses transformers to represent protein sequences as text. This breakthrough enables a sequence-to-property prediction for peptides without relying on explicit structural data. Inspired by the recent progress in the field of large language models, we present PeptideBERT, a protein language model specifically tailored for predicting essential peptide properties such as hemolysis, solubility, and nonfouling. The PeptideBERT utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. Through fine-tuning the pretrained model for the three downstream tasks, our model is state of the art (SOTA) in predicting hemolysis, which is crucial for determining a peptide's potential to induce red blood cells as well as nonfouling properties. Leveraging primarily shorter sequences and a data set with negative samples predominantly associated with insoluble peptides, our model showcases remarkable performance.

Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach.

Dynamics of individual amino acids play key roles in the overall properties of proteins. However, the knowledge of protein structural features at the residue level is limited due to the current resolutions of experimental and computational techniques. To address this issue, we designed a novel machine-learning (ML) framework that uses Molecular Dynamics (MD) trajectories to identify the major conformational states of individual amino acids, classify amino acids switching between two distinct modes, and evaluate their degree of dynamic stability. The Random Forest model achieved 96.94% classification accuracy in identifying switch residues within proteins. Additionally, our framework distinguishes between the stable switch (SS) residues, which remain stable in one angular state and jump once to another state during protein dynamics, and unstable switch (US) residues, which constantly fluctuate between the two angular states. This study also illustrates the correlation between the dynamics of SS residues and the protein's global properties.

Activity Map and Transition Pathways of G Protein-Coupled Receptor Revealed by Machine Learning.

Approximately, one-third of all U.S. Food and Drug Administration approved drugs target G protein-coupled receptors (GPCRs). However, more knowledge of protein structure-activity correlation is required to improve the efficacy of the drugs targeting GPCRs. In this study, we developed a machine learning model to predict the activation state and activity level of the receptors with high prediction accuracy. Furthermore, we applied this model to thousands of molecular dynamics trajectories to correlate residue-level conformational changes of a GPCR to its activity level. Finally, the most probable transition pathway between activation states of a receptor can be identified using the state-activity information. In addition, with this model, we can associate the contribution of each amino acid to the activation process. Using this method, we can design drugs that mainly target principal amino acids driving the transition between activation states of GPCRs. Our advanced method is generalizable to all GPCR classes and provides mechanistic insight into the activation mechanism in the receptors.

Prediction of GPCR activity using machine learning.

GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure-activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively describe the important structural features in the GPCR and correlate them to the activation state of GPCR. In this study, we developed 3 ML approaches to predict the conformation state of GPCR proteins. Additionally, we predict the activity level of GPCRs based on their structure. We leverage the unique advantages of each of the 3 ML approaches, interpretability of XGBoost, minimal feature engineering for 3D convolutional neural network, and graph representation of protein structure for graph neural network. By using these ML approaches, we are able to predict the activation state of GPCRs with high accuracy (91%-95%) and also predict the activation state of GPCRs with low error (MAE of 7.15-10.58). Furthermore, the interpretation of the ML approaches allows us to determine the importance of each of the features in distinguishing between the GPCRs conformations.

The Effect of Cyberspace-Based Education on the Well-Being of Caregivers of Elderly People with Dementia.

INTRODUCTION: One of the consequences of aging is the prevalence of chronic and age-related diseases, such as dementia. Caring for patients with dementia has a negative impact on the caregiver's well-being. This study aimed to examine the impact of cyberspace-based education on the well-being of caregivers of demented elderly people. METHODS: This experimental study was done on a sample of 86 caregivers of the elderly with dementia in 2018. The study sample was selected from the memory clinic of Taleghani Hospital and randomly assigned into groups (intervention n = 43, control n = 43 groups). The well-being was measured using the World Health Organization - Five Well-Being Index (WHO-5) before and two months after the intervention. The cyberspace-based educational intervention was conducted for one month. The SPSS software version 23 was employed in data analysis. RESULTS: The mean age of the caregivers in the intervention and control groups were M = 51.95, SD = 10.90 and M = 51.36, SD = 15.12 respectively. No significant difference was found between the two groups in terms of age, gender, and level of education. The results of the analysis showed that while the well-being of the intervention group was significantly increased (t (38) = -11.38, P<0.001), the well-being in the control group was significantly reduced (t(36) =4.71, P<0.001). CONCLUSION: The findings showed that cyberspace-based education could improve the well-being of caregivers of the elderly with dementia.

Are health websites credible enough for elderly self-education in the most prevalent elderly diseases?

BACKGROUND: The Elderly and their caregivers need credible health information to manage elderly chronic diseases and help them to be involved in health decision making. In this regard, health websites are considered as a potential source of information for elderlies as well as their caregivers. Nevertheless, the credibility of these websites has not been identified yet. Thus, this study aimed to evaluate the credibility of the health websites on the most prevalent chronic diseases of the elderly. METHODS: The terms "Chronic obstructive pulmonary disease", "Alzheimer's", "Ischemic heart disease", and "Stroke" were searched using the three popular search engines. A total of 216 unique websites were eligible for evaluation. The study was carried out using the HONcode of conduct. The chi-square test was carried out to determine the difference between conforming and nonconforming websites with HONcode principles and website categories. RESULTS: The findings showed that half of the evaluated websites had fully considered the HONcode principles. Furthermore, there was a significant difference between websites category and compliance with HONcode principles (p value < .05). CONCLUSION: Regarding the poor credibility of most prevalent elderly diseases' websites, the potential online health information users should be aware of the low credibility of such websites, which may seriously threaten their health. Furthermore, educating the elderly and their caregivers to evaluate the credibility of websites by the use of popular tools such as HONcode of conducts before utilizing their information seems to be necessary.

Proper measurement of pure dielectrophoresis force acting on a RBC using optical tweezers.

The force experienced by a neutral dielectric object in the presence of a spatially non-uniform electric field is referred to as dielectrophoresis (DEP). The proper quantification of DEP force in the single-cell level could be of great importance for the design of high-efficiency micro-fluidic systems for the separation of biological cells. In this report we show how optical tweezers can be properly utilized for proper quantification of DEP force experienced by a human RBC. By tuning the temporal frequency of the applied electric field and also performing control experiments and comparing our experimental results with that of theoretically calculated, we show that the measured force is a pure DEP force. Our results show that in the frequency range of 0.1-3 M H z the DEP force acting on RBC is frequency independent.