Understanding protein dynamics are essential for deciphering protein functional mechanisms and developing molecular therapies. However, the complex high-dimensional dynamics and interatomic interactions of biological processes pose significant challenge for existing computational techniques. In this paper, we approach this problem for the first time by introducing Deep Signature, a novel computationally tractable framework that characterizes complex dynamics and interatomic interactions based on their evolving trajectories. Specifically, our approach incorporates soft spectral clustering that locally aggregates cooperative dynamics to reduce the size of the system, as well as signature transform that collects iterated integrals to provide a global characterization of the non-smooth interactive dynamics. Theoretical analysis demonstrates that Deep Signature exhibits several desirable properties, including invariance to translation, near invariance to rotation, equivariance to permutation of atomic coordinates, and invariance under time reparameterization. Furthermore, experimental results on three benchmarks of biological processes verify that our approach can achieve superior performance compared to baseline methods.

Covid-19 has become a world pandemic for years. With the appearance of mutations, immune escape has become a problem, reducing the effectiveness of vaccines and antibodies. To reveal the mechanism of immune escape, we analyze the geometrical properties of the receptor-binding domain in the SARS-CoV-2 spike protein, which plays a vital role in the immune reaction. Several important variants are taken as examples, and the wild type model is prepared as a reference. The computational method is applied to simulate the behaviors of the models, and alpha shape algorithm is employed to extract geometrical data of the protein surface. Average moving distance of the surface atoms is used to quantify their activity. Our results show that the mutations changed the properties of the protein. The variants have different distributions of active sites, which may change the specific antigenicity and influence the binding abilities of drugs and antibodies. This study explains the mechanism of immune escape of SARS-CoV-2, and provides a geometrical method to find potential new target sites for the design of drugs and vaccines.

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.

Non-small cell lung cancer (NSCLC) caused by mutation of the epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) of EGFR have been developed and show promising results at the initial stage of therapy. However, in most cases, their efficacy becomes limited due to the emergence of secondary mutations causing drug resistance after about a year. In this work, we investigated the mechanism of drug resistance due to these mutations. We performed molecular dynamics (MD) simulations of EGFR-drug interactions to obtain Euclidean distance and binding free energy values to analyse drug resistance and visualize drug-protein interactions. A PCA-based method is proposed to find normal, rigid, flexible, and critical residues. We have established a systematic method for the visualization of protein-drug interactions, which provides an effective framework for the analysis of drug resistance in lung cancer at the atomic level.

Non-small cell lung cancer (NSCLC) with activating epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) have been developed to target the EGFR, stop the downstream signaling and the tumor growth. Despite of initial good results, drug resistance is developed after one year due to a secondary mutation. The L858R, T790M mutation and their combination change the conformational redistribution of EGFR. To combat drug resistance caused by the T790M mutation, AZD9291 third generation drug was approved by the food and drug administration agency, FDA, USA. However, resistance to AZD9291 is developed due to C797S mutation. In this paper, we investigate the drug resistance due to these genomic variations. We perform molecular dynamics (MD) simulation for EGFR, EGFR with L858R single point mutation, EGFR with L858R and T790M double point mutation and EGFR with L858R, T790M and C797S triple point mutation. We apply principal component analysis PCA and clustering to the atomic trajectories of EGFR and its mutants and extract the dominant motions. The first PC captures 29.04%, 51.17% 53.79% and 51.67% variance in WT, L858R, T790M and C797S mutants, respectively. First 20 PCs are used to explain the dynamics of the system, that captures about 90% of the variance in the system. This shows that the mutation increases the variance which leads to structural and dynamical changes and can be one of the reasons for the drug resistance. Our results provide new insights to the conformational dynamics and structural changes in EGFR and its mutants, that can be helpful for understanding the drug resistance mechanism and designing future therapies for NSCLC patients.