Biology subjects

Cava, J. K.

Publications and source records attributed to Cava, J. K..

3 recordsLinked to original sources

bioRxiv2022

Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

In this paper, we develop a formulation to utilize reinforcement learning and sampling-based robotics planning to derive low free energy transition pathways between two known states. Our formulation uses Jarzynskis equality and the stiffspring approximation to obtain point estimates of energy, and construct an informed path search with atomistic resolution. At the core of this framework, is our first ever attempt we use a policy driven adaptive steered molecular dynamics (SMD) to control our molecular dynamics simulations. We show that both the reinforcement learning and robotics planning realization of the RL-guided framework can solve for pathways on toy analytical surfaces and alanine dipeptide.

biophysics↗

bioRxiv2020

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

Molecular dynamics (MD) simulations have emerged to become the back-bone of todays computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in-tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame-works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of resolving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

biophysics↗

bioRxiv2019

WITOD: A Tool for Within-Taxon Operational Taxonomic Unit Diversity Analysis

MotivationTaxonomic analysis of microbiomes based on 16S rRNA amplicons has so far been usually restricted to abundance analysis of operational taxonomic units (OTUs), which are usually mapped to genus, as the furthest level for a balance between accuracy and speed. Biodiversity within taxa has been little studied, due to lack of proper computational tools. Within-taxon diversity reflects genetic polymorphism within the taxa to reveal whether there are potentially more variants/lineages for some microbiome-associated traits, e.g., diseases or bioconversion efficiency. The diversity will aid diagnosis decisions.\n\nResultsHere we introduce a tool, WITOD, for the WIthin-Taxon Operational taxonomic unit Diversity analysis. WITOD works on the alignment of all the OTU sequences within a taxon to get a non-redundant alignment with consensus regions on both ends (due to the conservation of the 16S regions used). Then the relative abundance of identical OTUs is combined; more specific taxonomy is obtained through BLASTn of these OTU sequences against the Silva database. One of the outputs is an OTU table with these unique OTUs with new taxonomy, combined relative abundance. In addition, another table can be constructed which we call the diversity table that associates the number of OTUs for a specific Taxa and the relative abundance in that environment. This can be used to further discover the importance of differing diversity in a given environment.\n\nConclusionsIn this paper, we have introduced a python program WITOD which is a computational tool to analyze the within-taxon biodiversity of microbiome composition. It will be useful in revealing the genetic polymorphism in general and aiding identifying the causative human pathogens or environmental disease vectors. WITOD is available at https://github.com/johncava/WITOD

bioinformatics↗