The eBDIMS path-sampling server: generation, classification and interactive visualization of protein ensembles and transition pathways in 2D-motion space
The recent rise of cryo-EM and X-ray high-throughput techniques is providing a wealth of new structures trapped in different conformations. Understanding how proteins transition between different conformers, and how they relate to each other in terms of function is not straightforward, and highly depends on the choice of the right set of degrees of freedom. Here we present eBDIMS server, an online tool and software for automatic classification of structural ensembles and reconstruction of transition pathways using coarse-grained (CG) simulations. The server generates CG-pathways between two protein conformations along with a representation in a simplified 2D-motion landscape based on the Principal Components (PCs) from experimental structures. For a conformationally rich ensemble, the PCs provide powerful reaction coordinates for automatic structure classification, detection of on-pathway intermediates and validation of in silico pathways. When the number of available structures is low or sampling is limited, Normal Modes (NMs) provide alternative motion axes for trajectory analysis. The path-generation eBDIMS method is available at a user-friendly website: https://login.biophysics.kth.se/eBDIMS/ or as standalone software. The server incorporates a powerful interactive graphical interface for simultaneous visualization of transition pathways in 2D-motion space and 3D-molecular graphics, which greatly facilitates the exploration of the relationships between different conformations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning
Medulloblastoma (MB) is a primary central nervous system tumor and the most common malignant brain cancer among children. Neuropathologists perform microscopic inspection of histopathological tissue slides under a micros…
ClassificationTransfer LearningAutomated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Accurate assessment of immunohistochemi…
DiagnosticPrognosisChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling
Although prevailing camera-controlled video generation models can produce cinematic results, lifting them directly to the generation of 3D-consistent and high-fidelity time-synchronized multi-view videos remains challeng…
Data AugmentationVideo GenerationDistributed Time- and Event-Triggered Observers for Linear Systems: Non-Pathological Sampling and Inter-Event Dynamics
For an autonomous linear time-invariant (LTI) system, a distributed observer with time-triggered periodic observations and event-triggered communication is proposed to estimate the state of the system. It is shown that t…
A Whole Slide Image Grading Benchmark and Tissue Classification for Cervical Cancer Precursor Lesions with Inter-Observer Variability
The cervical cancer developing from the precancerous lesions caused by the Human Papilloma Virus (HPV) has been one of the preventable cancers with the help of periodic screening. There are two types of grading conventio…
General Classification