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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.

The RAS proteins are GTP-dependent switches that regulate signaling pathways and are frequently mutated in cancer. RAS proteins concentrate in the plasma membrane via lipid-tethers and hypervariable region side-chain interactions in distinct nano-domains. However, little is known about RAS membrane dynamics and the details of RAS activation of downstream signaling. Here, we characterize RAS in live human and mouse cells using single-molecule-tracking methods and estimate RAS mobility parameters. KRAS4b exhibits confined mobility with three diffusive states distinct from the other RAS isoforms (KRAS4a, NRAS, and HRAS); and although most of the amino acid differences between RAS isoforms lie within the hypervariable region, the additional confinement of KRAS4b is largely determined by the protein's globular domain. To understand the altered mobility of an oncogenic KRAS4b, we used complementary experimental and molecular dynamics simulation approaches to reveal a detailed mechanism.

Patient-specific models of the ventricular myocardium, combined with the computational power to run rapid simulations, are approaching the level where they could be used for personalized cardiovascular medicine. A major remaining challenge is determining model parameters from available patient data, especially for models of the Purkinje-myocardial junctions (PMJs): the sites of initial ventricular electrical activation. There are no non-invasive methods for localizing PMJs in patients, and the relationship between the standard clinical ECG and PMJ model parameters is underexplored. Thus, this study aimed to determine the sensitivity of the QRS complex of the ECG to the anatomical location and regional number of PMJs. The QRS complex was simulated using an image-based human torso and biventricular model, and cardiac electrophysiology was simulated using Cardioid. The PMJs were modeled as discrete current injection stimuli, and the location and number of stimuli were varied within initial activation regions based on published experiments. Results indicate that the QRS complex features were most sensitive to the presence or absence of four "seed" stimuli, and adjusting locations of nearby "regional" stimuli provided finer tuning. Decreasing number of regional stimuli by an order of magnitude resulted in virtually no change in the QRS complex. Thus, a minimal 12-stimuli configuration was identified that resulted in physiological excitation, defined by QRS complex feature metrics and ventricular excitation pattern. Overall, the sensitivity results suggest that parameterizing PMJ location, rather than number, be given significantly higher priority in future studies creating personalized ventricular models from patient-derived ECGs.

Use of the highly toxic and easily prepared rodenticide tetramethylenedisulfotetramine (TETS) was banned after thousands of accidental or intentional human poisonings, but it is of continued concern as a chemical threat agent. TETS is a noncompetitive blocker of the GABA type A receptor (GABAAR), but its molecular interaction has not been directly established for lack of a suitable radioligand to localize the binding site. We synthesized [(14)C]TETS (14 mCi/mmol, radiochemical purity >99%) by reacting sulfamide with H(14)CHO and s-trioxane then completion of the sequential cyclization with excess HCHO. The outstanding radiocarbon sensitivity of accelerator mass spectrometry (AMS) allowed the use of [(14)C]TETS in neuroreceptor binding studies with rat brain membranes in comparison with the standard GABAAR radioligand 4'-ethynyl-4-n-[(3)H]propylbicycloorthobenzoate ([(3)H]EBOB) (46 Ci/mmol), illustrating the use of AMS for characterizing the binding sites of high-affinity (14)C radioligands. Fourteen noncompetitive antagonists of widely diverse chemotypes assayed at 1 or 10 µM inhibited [(14)C]TETS and [(3)H]EBOB binding to a similar extent (r(2) = 0.71). Molecular dynamics simulations of these 14 toxicants in the pore region of the α1β2γ2 GABAAR predict unique and significant polar interactions for TETS with α1T1' and γ2S2', which are not observed for EBOB or the GABAergic insecticides. Several GABAAR modulators similarly inhibited [(14)C]TETS and [(3)H]EBOB binding, including midazolam, flurazepam, avermectin Ba1, baclofen, isoguvacine, and propofol, at 1 or 10 μM, providing an in vitro system for recognizing candidate antidotes.

Despite their large size, antibodies have proven to be suitable radioisotope carriers to deliver systemic radiotherapy, often molecular image-based, for lymphoma and leukemia. To mimic antibody (Ab) targeting behavior while decreasing size by 50-100x, a combination of computational and experimental methods were used to generate molecules that bind to unique sites within the HLA-DR epitopic region of Lym-1, an Ab shown effective in patients. Lym-1 Ab mimics (synthetic high afinity ligands; SHALs) were generated and studied in vitro, using live cell binding assays, and/or pharmacokinetic studies over 24 h in xenografted mice given 1 or 20 mu g SHAL doses i.v. Multimilligram amounts of each of the dimeric (bis) SHALs were synthesized at high purity, and labeled with indium-111 at high specific activity and purity. These SHALs were selective for HLA-DR and HLA-DR expressing malignant cells and had functional affinities that ranged from 10(-9) M (nanomolar) to 10(-10) M. Blood clearances ranged from 3.6 to 9.5 h and body clearances ranged from 15.2 to 43.0 h for the 6 his DOTA-SHALs studied in a mouse model for non-Hodgkin's lymphoma (NHL). While localization was shown in Raji NHL xenografts, biodistribution was influenced by 'sinks' for individual ligands of the SHALs. Highly pure, dimeric mimics for HLA-DR Ab were synthesized, biotinylated and. radiolabeled, and showed selectivity in vitro. Pharmacokinetic behavior in mice was influenced by the ligands and by the linker length of the dimeric SHALs. Nanomolar or better functional affinity was observed when a suitably long linker was used to connect the two bidentate SHALs. The concept and methodology are of interest because applicable for targeting most proteins; the SHAL synthetic platform is highly efficient and adaptive.

Despite their large size, antibodies (Abs) are suitable carriers to deliver systemic radiotherapy, often molecular image-based, for lymphoma and leukemia. Lym-1 Ab has proven to be an effective radioisotope carrier, even in small amounts, for targeting human leukocyte antigen DR (HLA-DR), a surface membrane protein overexpressed on B-cell lymphoma. Pairs of molecules (referred to as ligands), shown by computational and experimental methods to bind to each of 2 sites within the Lym-1 epitopic region, have been linked to generate small (kDa) molecules (referred to as selective high-affinity ligands [SHALs]) to mimic the targeting properties of Lym-1 Ab. Methods: A lysine-polyethylene glycol (PEG) backbone was used to synthetically link 2 of the following ligands: deoxycholate, 5-leuenkephalin, triiodothyronine, thyronine, dabsyl-L-valine, and N-benzoyl-L-arginyl-4amino-benzoic acid to generate a series of 13 bidentate SHALs with a biotin or 1,4,7,10-tetraazacyclododecane-NN',N '',N'''-tetraacetic acid (DOTA) chelate attached to the linker. These SHALs have been assessed for their selectivity in binding to HLA-DR10-expressing cells and for their pharmacokinetics and tissue biodistribution in mice. Biotinylated versions of these SHALs discriminated cell lines positive for HLA-DR10 expression with near-nanomolar affinity. The DOTA versions of 4 SHALs were labeled with In-111 for pharmacokinetic studies in mice with HLA-DR10-expressing malignant Raji xenografts. Results: The bidentate, biotinylated, and DOTA-SHALs were synthesized in high-purity, multimilligram amounts. Mean radiochemical and product yields and purities were 90%, 75%, and 90% at mean specific activities of 3.9 MBq/mu g (105 mu Ci/mu g) for the In-111-labeled SHALs. As expected, rapid blood clearance and tumor targeting were observed. The pharmacokinetics of the SHALs was influenced by the component ligands. Biliary clearance, kidney localization, and serum receptor binding contributed to less favorable tumor targeting. Conclusion: A series of SHALs was readily synthesized in multimilligram amounts and showed the expected selective binding in vitro. Better selection of the SHAL components should provide second-generation SHALs with improved properties to fulfill the substantial potential of these novel molecular carriers for targeting.

We have implemented the Martini force field within Lawrence Livermore National Laboratory's molecular dynamics program, ddcMD. The program is extended to a heterogeneous programming model so that it can exploit graphics processing unit (GPU) accelerators. In addition to the Martini force field being ported to the GPU, the entire integration step, including thermostat, barostat, and constraint solver, is ported as well, which speeds up the simulations to 278-fold using one GPU vs one central processing unit (CPU) core. A benchmark study is performed with several test cases, comparing ddcMD and GROMACS Martini simulations. The average performance of ddcMD for a protein–lipid simulation system of 136k particles achieves 1.04 µs/day on one NVIDIA V100 GPU and aggregates 6.19 µs/day on one Summit node with six GPUs. The GPU implementation in ddcMD offloads all computations to the GPU and only requires one CPU core per simulation to manage the inputs and outputs, freeing up remaining CPU resources on the compute node for alternative tasks often required in complex simulation campaigns. The ddcMD code has been made open source and is available on GitHub at https://github.com/LLNL/ddcMD. [ABSTRACT FROM AUTHOR]