Biology subjects

Ballesteros, O. R.

Publications and source records attributed to Ballesteros, O. R..

2 recordsLinked to original sources

bioRxiv2022

Molecular dynamics simulations of the calmodulin-induced alpha-helix in the SK2 calcium-gated potassium ion channel

The family of small-conductance (SK) ion channels is composed of four members (SK1, SK2, SK3, and SK4) involved in neuron-firing regulation. The gating of these channels depends on the intracellular Ca2+ concentration, and their sensitivity to this ion is provided by calmodulin (CaM). This protein binds to a specific region in SK channels known as the calmodulin-binding domain (CaMBD), an event which is essential for their gating. While CaM-binding domains are typically disordered in the absence of CaM, the SK2 channel subtype displays a small pre-folded -helical region in its CaMBD even if CaM is not present. This small helix is known to turn into a full -helix upon CaM binding, although the molecular-level details for this conversion are not fully understood yet. In this work, we offer new insights on this physiologically relevant process by means of enhanced sampling, atomistic Hamiltonian replica exchange molecular dynamics simulations, providing a more detailed understanding of CaM binding to this target. Our results show that CaM is necessary for inducing a full -helix along the SK2 CaMBD through hydrophobic interactions with V426 and L427. However, it is also necessary that W431 does not compete for these interactions; the role of the small pre-folded -helix in the SK2 CaMBD would be to stabilize W431 so that this is the case.

biophysics↗

bioRxiv2022

Improved KCNQ2 gene missense variant interpretation with artificial intelligence

Advances in DNA sequencing technologies have revolutionized rare disease diagnosis, resulting in an increasing volume of available genomic data. Despite this wealth of information and improved procedures to combine data from various sources, identifying the pathogenic causal variants and distinguishing between severe and benign variants remains a key challenge. Mutations in the Kv7.2 voltage-gated potassium channel gene (KCNQ2) have been linked to different subtypes of epilepsies, such as benign familial neonatal epilepsy (BFNE) and epileptic encephalopathy (EE). To date, there is a wide variety of genome-wide computational tools aiming at predicting the pathogenicity of variants. However, previous reports suggest that these genome-wide tools have limited applicability to the KCNQ2 gene related diseases due to overestimation of deleterious mutations and failure to correctly identify benign variants, being, therefore, of limited use in clinical practice. In this work, we found that combining readily available features, such as AlphaFold structural information, Missense Tolerance Ratio (MTR) and other commonly used protein descriptors, provides foundations to build reliable gene-specific machine learning ensemble models. Here, we present a transferable methodology able to accurately predict the pathogenicity of KCNQ2 missense variants with unprecedented sensitivity and specificity scores above 90%.

pathology↗