Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review.

BACKGROUND: Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. OBJECTIVE: This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. METHODS: The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria. RESULTS: We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

Delineating the conformational dynamics of intermediate structures on the unfolding pathway of ß-lactoglobulin in aqueous urea and dimethyl sulfoxide.

The funnel shaped energy landscape model of the protein folding suggests that progression of folding proceeds through multiple pathways, having the multiple intermediates which leads to multidimensional free-energy surface. Herein, we applied all-atom MD simulation to conduct a comparative study on the structure of ß-lactoglobulin (ß-LgA) in aqueous mixture of 8 M urea and 8 M dimethyl sulfoxide (DMSO), at different temperatures. The cumulative results of multiple simulations suggest a common unfolding pathway of ß-LgA, occurred through the stable and meta-stable intermediates (I), in both urea and DMSO. However, the free-energy landscape (FEL) analyses show that the structural transitions of I-states are energetically different. In urea, FEL shows distinct ensemble of intermediates, I1 and I2, separated by the energy barrier of ∼3.0 kcal mol-1. Similarly, we find the population of two distinct I1 and I2 states in DMSO, however, the I1 appeared transiently around ∼30-35 ns and is short-lived. But, the I2 ensemble is observed structurally compact and long-lived (∼50-150 ns) as compared to unfolding in urea. Furthermore, the I1 and I2 are separated through a high energy barrier of ∼6.0 kcal mol-1. Thus, our results provide the structural insights of intermediates which essentially bear the signature of a different unfolding pathway of ß-LgA in urea and DMSO.Abbreviationsß-LgAß-lactoglobulinDMSOdimethyl sulfoxideFELfree-energy landscapeGdmClguanidinium chlorideIintermediate stateMGmolten globule statePMEparticle mesh EwaldQfraction of native contactsRMSDroot mean square deviationRMSFroot mean square fluctuationRgradius of gyrationSASAsolvent Accessible Surface AreascSASAthe side chain SASATrptryptophanCommunicated by Ramaswamy H. Sarma.