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The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Severe malarial anemia (SMA) is a leading cause of childhood morbidity and mortality in holoendemic Plasmodium falciparum transmission regions. To gain enhanced understanding of predisposing factors for SMA, we explored the relationship between complement component 3 (C3) missense mutations [rs2230199 (2307C>G, Arg>Gly102) and rs11569534 (34420G>A, Gly>Asp1224)], malaria, and SMA in a cohort of children (n = 1617 children) over 36 months of follow-up. Variants were selected based on their ability to impart amino acid substitutions that can alter the structure and function of C3. The 2307C>G mutation results in a basic to a polar residue change (Arg to Gly) at position 102 (β-chain) in the macroglobulin-1 (MG1) domain, while 34420G>A elicits a polar to acidic residue change (Gly to Asp) at position 1224 (α-chain) in the thioester-containing domain. After adjusting for multiple comparisons, longitudinal analyses revealed that inheritance of the homozygous mutant (GG) at 2307 enhanced the risk of SMA (RR = 2.142, 95%CI: 1.229–3.735, P = 0.007). The haplotype containing both wild-type alleles (CG) decreased the incident risk ratio of both malaria (RR = 0.897, 95%CI: 0.828–0.972, P = 0.008) and SMA (RR = 0.617, 95%CI: 0.448–0.848, P = 0.003). Malaria incident risk ratio was also reduced in carriers of the GG (Gly102Gly1224) haplotype (RR = 0.941, 95%CI: 0.888–0.997, P = 0.040). Collectively, inheritance of the missense mutations in MG1 and thioester-containing domain influence the longitudinal risk of malaria and SMA in children exposed to intense Plasmodium falciparum transmission.

High dimensional predictive models of Major Adverse Cardiac Events (MACE), which includes heart attack (AMI), stroke, and death caused by cardiovascular disease (CVD), were built using four longitudinal cohorts of Veterans Administration (VA) patients created from VA medical records. We considered 247 variables / risk factors measured across 7.5 years for millions of patients in order to compare predictions for the first reported MACE event using six distinct modelling methodologies. The best-performing methodology varied across the four cohorts. Model coefficients related to disease pathophysiology and treatment were relatively constant across cohorts, while coefficients dependent upon the confounding variables of age and healthcare utilization varied considerably across cohorts. In particular, models trained on a retrospective case-control (Rcc) cohort (where controls are matched to cases by date of birth cohort and overall level of healthcare utilization) emphasize variables describing pathophysiology and treatment, while predictions based on the cohort of all active patients at the start of 2017 (C-17) rely much more on age and variables reflecting healthcare utilization. In consequence, directly using an Rcc-trained model to evaluate the C-17 cohort resulted in poor performance (C-statistic = 0.65). However, a simple reoptimization of model dependence on age, demographics, and five other variables improved the C-statistic to 0.74, nearly matching the 0.76 obtained on C-17 by a C-17-trained model. Dependence of MACE risk on biomarkers for hypertension, cholesterol, diabetes, body mass index, and renal function in our models was consistent with the literature. At the same time, including medications and procedures provided important indications of both disease severity and the level of treatment. More detailed study designs will be required to disentangle these effects.

Discovery of reliable signatures for the empirical diagnosis of neurological diseases – both infectious and non-infectious – remains unrealized. One of the primary challenges encountered in such studies is the lack of a comprehensive database representative of a signature background that exists in healthy individuals, and against which an aberrant event can be assessed. For neurological insults and injuries, it is important to understand the normal profile in the neuronal (cerebrospinal fluid) and systemic fluids (e.g., blood). Here, we present a comparative multi-omic human database of of serum and cerebrospinal fluid signatures derived from a population of 30 individuals (15 males, 15 females, 23–74 years) as a first step towards establishing a comprehensive database in future. In addition to empirical signatures, we also assigned common pathways between serum and CSF. Together, our findings provide a strategy to establish a biomarker baseline against which aberrant signature profiles in individuals with neurological injuries/disease can be assessed – providing a pathway for comprehensive diagnostics and therapeutics discovery.

Significance Here we present an unprecedented multiscale simulation platform that enables modeling, hypothesis generation, and discovery across biologically relevant length and time scales to predict mechanisms that can be tested experimentally. We demonstrate that our predictive simulation-experimental validation loop generates accurate insights into RAS-membrane biology. Evaluating over 100,000 correlated simulations, we show that RAS–lipid interactions are dynamic and evolving, resulting in: 1) a reordering and selection of lipid domains in realistic eight-lipid bilayers, 2) clustering of RAS into multimers correlating with specific lipid fingerprints, 3) changes in the orientation of the RAS G-domain impacting its ability to interact with effectors, and 4) demonstration that RAS–RAS G-domain interfaces are nonspecific in these putative signaling domains., RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present two models for quantifying the strength of selection for new and emerging variants of SARS-CoV-2 relative to the background of contemporaneous variants. These methods range from a detailed model of dynamics within one country to a broad analysis across all countries, and they include alternative explanations such as migration and drift. We find evidence for strong selection favoring the D614G spike mutation and B.1.1.7 (Alpha), weaker selection favoring B.1.351 (Beta), and no advantage of R.1 after it spreads beyond Japan. Cutting back data to earlier time horizons reveals that uncertainty is large very soon after emergence, but that estimates of selection stabilize after several weeks. Our results also show substantial heterogeneity among countries, demonstrating the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2., Identifying new SARS-CoV-2 genetic variants of concern is important to inform public health strategies, but distinguishing those causing a significant threat is challenging. Here, the authors develop and evaluate two models for assessing the strength of selection for new variants.