Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.

Peptides are crucial in biological processes and therapeutic applications. Given their importance, advancing our ability to predict peptide properties is essential. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with graph neural networks (GNNs) to predict peptide properties.

IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins Using Large Language Models.

Intrinsically disordered Proteins (IDPs) constitute a large and structureless class of proteins with significant functions. The existence of IDPs challenges the conventional notion that the biological functions of proteins rely on their three-dimensional structures. Despite lacking well-defined spatial arrangements, they exhibit diverse biological functions, influencing cellular processes and shedding light on disease mechanisms.

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.

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).

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.

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.

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).

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.

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.

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.

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.