Molecular Dynamics (MD) simulations are a crucial tool for understanding how proteins fold. In its easiest form, MD simulations can be scaled through data parallelism, this means that multiple folding trajectories can be spawned and executed in parallel, facilitating a more efficient exploration of the protein folding space. However, due to data dependencies, the analysis of MD simulations remains largely as a centralized process. In this work, we propose a data parallel, lightweight technique to learn the characteristics of protein folding states in MD simulations. Contrary to other methods, ours can differentiate relevant states in a single protein folding trajectory without requiring centralized global knowledge of the protein dynamics. As its processing and memory overheads are negligible (in the order of milliseconds per window of frames, and kilo bytes respectively) this technique can be coupled with the simulation for in-situ analysis.

In order to successfully predict a proteins function throughout its trajectory, in addition to uncovering changes in its conformational state, it is necessary to employ techniques that maintain its 3D information while performing at scale. We extend a protein representation that encodes secondary and tertiary structure into fix-sized, color images, and a neural network architecture (called GEM-net) that leverages our encoded representation. We show the applicability of our method in two ways: (1) performing protein function prediction, hitting accuracy between 78 and 83 percent, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.

Molecular Dynamics (MD) simulations executed on state-of-the-art supercomputers are producing data at rates faster than it can be written out to disk. In situ and in transit analysis of data generated by MD simulations reduce the original volume of information by several orders of magnitude, thereby alleviating the negative impact of I/O bottlenecks. This work focuses on characterizing the impact of in situ and in transit analytics on the overall MD workflow performance, and the capability for capturing rapid, rare events in the simulated molecular system. The MD simulation and analysis processes share data via remote direct memory access (RDMA) using DataSpaces. Our metrics of interest are time spent waiting in I/O by the MD simulation, lost frames of the MD simulation, and idle time of the analysis. We measure these metrics for a diverse set of molecular systems and characterize their trends for in situ and in transit configurations. We then model which frames are dropped and which ones are analyzed for a real use case. The insights gained from this study are generally applicable for in situ and in transit workflows that require optimization of parameters to minimize loss in workflow performance and analytic accuracy.

Complex networks of interacting residues and microdomains in the structures of biomolecular systems underlie the reliable propagation of information from an input signal, such as the concentration of a ligand, to sites that generate the appropriate output signal, such as enzymatic activity. This information transduction often carries the signal across relatively large distances at the molecular scale in a form of allostery that is essential for the physiological functions performed by biomolecules. While allosteric behaviors have been documented from experiments and computation, the mechanism of this form of allostery proved difficult to identify at the molecular level. Here, we introduce a novel analysis framework, called N-body Information Theory (NbIT) analysis, which is based on information theory and uses measures of configurational entropy in a biomolecular system to identify microdomains and individual residues that act as (i)-channels for long-distance information sharing between functional sites, and (ii)-coordinators that organize dynamics within functional sites. Application of the new method to molecular dynamics (MD) trajectories of the occluded state of the bacterial leucine transporter LeuT identifies a channel of allosteric coupling between the functionally important intracellular gate and the substrate binding sites known to modulate it. NbIT analysis is shown also to differentiate residues involved primarily in stabilizing the functional sites, from those that contribute to allosteric couplings between sites. NbIT analysis of MD data thus reveals rigorous mechanistic elements of allostery underlying the dynamics of biomolecular systems.

From computational simulations of a serotonin 2A receptor (5-HT(2A)R) model complexed with pharmacologically and structurally diverse ligands we identify different conformational states and dynamics adopted by the receptor bound to the full agonist 5-HT, the partial agonist LSD, and the inverse agonist Ketanserin. The results from the unbiased all-atom molecular dynamics (MD) simulations show that the three ligands affect differently the known GPCR activation elements including the toggle switch at W6.48, the changes in the ionic lock between E6.30 and R3.50 of the DRY motif in TM3, and the dynamics of the NPxxY motif in TM7. The computational results uncover a sequence of steps connecting these experimentally-identified elements of GPCR activation. The differences among the properties of the receptor molecule interacting with the ligands correlate with their distinct pharmacological properties. Combining these results with quantitative analysis of membrane deformation obtained with our new method (Mondal et al, Biophysical Journal 2011), we show that distinct conformational rearrangements produced by the three ligands also elicit different responses in the surrounding membrane. The differential reorganization of the receptor environment is reflected in (i)-the involvement of cholesterol in the activation of the 5-HT(2A)R, and (ii)-different extents and patterns of membrane deformations. These findings are discussed in the context of their likely functional consequences and a predicted mechanism of ligand-specific GPCR oligomerization.

We present an integrated experimental and computational approach designed to identify the key cellular components that either contribute to or drive therapeutic synergy of drug combinations with anticancer activity. The approach includes (i) quantification of drug synergy in high throughput transcriptome experiments, (ii) data-driven reverse engineering and forward simulation technology to develop an in silico model predictive of drug synergy, and (iii) utilization of databases of interaction and functional information in hypothesis generation that are validated experimentally in a final step (iv). The goal is to develop an integrated framework that aids in understanding the mechanistic details of drug synergy to design better combination drugs. We illustrate this approach with an application to the analysis of transcriptome changes in cells exposed to the synergistic anticancer drug combination of farnesyl transferase inhibitors (FTIs) combined with taxanes.

To enable rigorous and reliable modeling of functional mechanisms of membrane proteins, the computational modeling and simulation approaches must take advantage, to the fullest extent possible, of the wealth of data obtained experimentally. This type of information makes it possible not only to construct rigorous molecular models and quantitative representations of function, but also to test, verify and refine them continuously. Among the proteins in the cell membrane, the family of Neurotransmitter: Sodium Symporter (NSS) exhibits highly complex mechanisms that involve multiple molecular states in the substrate and ion transport cycle. Due to their high biological and medical importance, experimental data for these systems is abundant, and efficient management of information from the literature and other relevant data sources promises to be very rewarding. We have developed an information management system (IMS) for data concerning the NSS proteins to enable retrieval, structured organization, and query of information available in the literature. The IMS supports the interplay between computational approaches and experimental studies of structure, function and physiological mechanisms of the NSS, and is of special utility in the integration of data of different types and various sources, and of the mechanistic understanding emerging from separate functional studies of individual members of the family.

SUMMARY: PDZBase is a database that aims to contain all known PDZ-domain-mediated protein-protein interactions. Currently, PDZBase contains approximately 300 such interactions, which have been manually extracted from > 200 articles. The database can be queried through both sequence motif and keyword-based searches, and the sequences of interacting proteins can be visually inspected through alignments (for the comparison of several interactions), or as residue-based diagrams including schematic secondary structure information (for individual complexes).

MOTIVATION: The dearth of structural data on alpha-helical membrane proteins (MPs) has hampered thus far the development of reliable knowledge-based potentials that can be used for automatic prediction of transmembrane (TM) protein structure. While algorithms for identifying TM segments are available, modeling of the TM domains of alpha-helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains. RESULTS: A recent increase in the number of crystal structures of alpha-helical MPs has enabled an analysis of the lipid-exposed surfaces and the interiors of such molecules on the basis of structure, rather than sequence alone. Together with a conservation criterion that is based on previous observations that conserved residues are mostly found in the interior of MPs, the bias of certain residue types to be preferably buried or exposed is proposed as a criterion for predicting the lipid-exposed and interior faces of TMs. Applications to known structures demonstrates 80% accuracy of this prediction algorithm. AVAILABILITY: The algorithm used for the predictions is implemented in the ProperTM Web server (http://icb.med.cornell.edu/services/propertm/start).

SUMMARY: Residue-based diagrams of proteins are graphical representations that can be used in protein information systems. These diagrams make it possible to visually integrate different types of biological information. The approach has been used successfully for membrane proteins. We developed the Residue-based diagram generator to (i) make it possible to generate residue-based diagrams of proteins in a batch mode that is compatible with the needs of information system curators, (ii) allow the generation of residue-based diagrams for families of soluble proteins or domains. AVAILABILITY: Licensed. Royalty free licenses are granted to non-profit institutions for educational and research purposes. http://icb.mssm.edu/crt/RbDg/index.xml