Mutation prediction in the SARS-CoV-2 genome using attention-based neural machine translation.

Severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) has been evolving rapidly after causing havoc worldwide in 2020. Since then, it has been very hard to contain the virus owing to its frequently mutating nature. Changes in its genome lead to viral evolution, rendering it more resistant to existing vaccines and drugs. Predicting viral mutations beforehand will help in gearing up against more infectious and virulent versions of the virus in turn decreasing the damage caused by them. In this paper, we have proposed different NMT (neural machine translation) architectures based on RNNs (recurrent neural networks) to predict mutations in the SARS-CoV-2-selected non-structural proteins (NSP), i.e., NSP1, NSP3, NSP5, NSP8, NSP9, NSP13, and NSP15. First, we created and pre-processed the pairs of sequences from two languages using k-means clustering and nearest neighbors for training a neural translation machine. We also provided insights for training NMTs on long biological sequences. In addition, we evaluated and benchmarked our models to demonstrate their efficiency and reliability.

Modeling of pH regulation in tumor cells: Direct interaction between proton-coupled lactate transporters and cancer-associated carbonic anhydrase.

The most aggressive tumor cells, which often reside in a hypoxic environment, can release vast amounts of lactate and protons via monocarboxylate transporters (MCTs). This additional proton efflux exacerbates extracellular acidification and supports the formation of a hostile environment. In the present study we propose a novel, data-based model for this proton-coupled lactate transport in cancer cells. The mathematical settings involve systems coupling nonlinear ordinary and stochastic differential equations describing the dynamics of intra- and extracellular proton and lactate concentrations. The data involve time series of intracellular proton concentrations of normoxic and hypoxic MCF-7 breast cancer cells. The good agreement of our final model with the data suggests the existence of proton pools near the cell membrane, which can be controlled by intracellular and extracellular carbonic anhydrases to drive proton-coupled lactate transport across the plasma membrane of hypoxic cancer cells.