Artificial intelligence-augmented analysis of contemporary procedural, mortality, and cost trends in carcinoid heart disease in a large national cohort with a focus on the "forgotten pulmonic valve".

Background: Carcinoid heart disease is increasingly recognized and challenging to manage due to limited outcomes data. This is the largest known cohort study of valvular pathology, treatment (including pulmonary and tricuspid valve replacements [PVR and TVR]), dispairties, mortality, and cost in patients with malignant carcinoid tumor (MCT). Methods: Machine learning-augmented propensity score-adjusted multivariable regression was conducted for clincal outcomes in the 2016-2018 U.S. National Inpatient Sample (NIS). Regression models were weighted by the complex survey design and adjusted for known confounders and the likelihood of undergoing valvular procedures. Results: Among 101,521,656 hospitalizations, 55,910 (0.06%) had MCT. Patients with MCT vs. those without had significantly higher inpatient mortality (2.93 vs. 2.04%, p = 0.002), longer mean length of stay (12.20 vs. 4.62, p < 0.001), and increased mean total cost of stay ($70,252.18 vs. 51,092.01, p < 0.001). There was a step-wise increased rate of TVR and PVR with each subsequent year, with significantly more TV (0.16% vs. 0.01, p < 0.001) and PV (0.03 vs. 0.00, p = 0.040) diagnosed with vs. without MCT for 2016, with comparable trends in 2017 and 2018. There were no significant procedural disparities among patients with MCT for sex, race, income, urban density, or geographic region, except in 2017, when the highest prevalence of PV procedures were performed in the Western North at 50.00% (p = 0.034). In machine learning and propensity score augmented multivariable regression, MCT did not significantly increase the likelihood of TVR or PVR. In sub-group analysis restricted to MCT, neither TVR nor PVR significantly increased mortality, though it did increase cost (respectively, $141,082.30, p = 0.015; $355,356.40, p = 0.012). Conclusion: This analysis reflects a favorable trend in recognizing the need for TVR and PVR in patients with MCT, with associated increased cost but not mortality. Our study also suggests that pulmonic valve pathology is increasingly recognized in MCT as reflected by the upward trend in PVRs. Further research and updated societal guidelines may need to focus on the "forgotten pulmonic valve" to improve outcomes and disparities in this understudied patient population.

Rapid sampling of molecular motions with prior information constraints.

Proteins are active, flexible machines that perform a range of different functions. Innovative experimental approaches may now provide limited partial information about conformational changes along motion pathways of proteins. There is therefore a need for computational approaches that can efficiently incorporate prior information into motion prediction schemes. In this paper, we present PathRover, a general setup designed for the integration of prior information into the motion planning algorithm of rapidly exploring random trees (RRT). Each suggested motion pathway comprises a sequence of low-energy clash-free conformations that satisfy an arbitrary number of prior information constraints. These constraints can be derived from experimental data or from expert intuition about the motion. The incorporation of prior information is very straightforward and significantly narrows down the vast search in the typically high-dimensional conformational space, leading to dramatic reduction in running time. To allow the use of state-of-the-art energy functions and conformational sampling, we have integrated this framework into Rosetta, an accurate protocol for diverse types of structural modeling. The suggested framework can serve as an effective complementary tool for molecular dynamics, Normal Mode Analysis, and other prevalent techniques for predicting motion in proteins. We applied our framework to three different model systems. We show that a limited set of experimentally motivated constraints may effectively bias the simulations toward diverse predicates in an outright fashion, from distance constraints to enforcement of loop closure. In particular, our analysis sheds light on mechanisms of protein domain swapping and on the role of different residues in the motion.

MolAxis: a server for identification of channels in macromolecules.

MolAxis is a freely available, easy-to-use web server for identification of channels that connect buried cavities to the outside of macromolecules and for transmembrane (TM) channels in proteins. Biological channels are essential for physiological processes such as electrolyte and metabolite transport across membranes and enzyme catalysis, and can play a role in substrate specificity. Motivated by the importance of channel identification in macromolecules, we developed the MolAxis server. MolAxis implements state-of-the-art, accurate computational-geometry techniques that reduce the dimensions of the channel finding problem, rendering the algorithm extremely efficient. Given a protein or nucleic acid structure in the PDB format, the server outputs all possible channels that connect buried cavities to the outside of the protein or points to the main channel in TM proteins. For each channel, the gating residues and the narrowest radius termed 'bottleneck' are also given along with a full list of the lining residues and the channel surface in a 3D graphical representation. The users can manipulate advanced parameters and direct the channel search according to their needs. MolAxis is available as a web server or as a stand-alone program at http://bioinfo3d.cs.tau.ac.il/MolAxis.

Computational characterization of B-cell epitopes.

Characterizing B-cell epitopes is a fundamental step for understanding the immunological basis of bio-recognition. To date, epitope analyses have either been based on limited structural data, or sequence data alone. In this study, our null hypothesis was that the surface of the antigen is homogeneously antigenic. To test this hypothesis, a large dataset of antibody-antigen complex structures, together with crystal structures of the native antigens, has been compiled. Computational methods were developed and applied to detect and extract physico-chemical, structural, and geometrical properties that may distinguish an epitope from the remaining antigen surface. Rigorous statistical inference was able to clearly reject the null hypothesis showing that epitopes are distinguished from the remaining antigen surface in properties such as amino acid preference, secondary structure composition, geometrical shape, and evolutionary conservation. Specifically, epitopes were found to be significantly enriched with tyrosine and tryptophan, and to show a general preference for charged and polar amino acids. Additionally, epitopes were found to show clear preference for residing on planar parts of the antigen that protrude from the surface, yet with a rugged surface shape at the atom level. The effects of complex formation on the structural properties of the antigen were also computationally characterized and it is shown that epitopes undergo compression upon antibody binding. This correlates with the finding that epitopes are enriched with unorganized secondary structure elements that render them flexible. Thus, this study extends the understanding of the underlying processes required for antibody binding, and reveals new aspects of the antibody-antigen interaction.

Generation, comparison, and merging of pathways between protein conformations: gating in K-channels.

We present a general framework for the generation, alignment, comparison, and hybridization of motion pathways between two known protein conformations. The framework, which is rooted in probabilistic motion-planning techniques in robotics, allows for the efficient generation of collision-free motion pathways, while considering a wide range of degrees of freedom involved in the motion. Within the framework, we provide the means to hybridize pathways, thus producing, the motion pathway of the lowest energy barrier out of the many pathways proposed by our algorithm. This method for comparing and hybridizing pathways is modular, and may be used within the context of molecular dynamics and Monte Carlo simulations. The framework was implemented within the Rosetta software suite, where the protein is represented in atomic detail. The K-channels switch between open and closed conformations, and we used the overall framework to investigate this transition. Our analysis suggests that channel-opening may follow a three-phase pathway. First, the channel unlocks itself from the closed state; second, it opens; and third, it locks itself in the open conformation. A movie that depicts the proposed pathway is available in the Supplementary Material (Movie S1) and at http://www.cs.tau.ac.il/~angela/SuppKcsA.html.

MolAxis: efficient and accurate identification of channels in macromolecules.

Channels and cavities play important roles in macromolecular functions, serving as access/exit routes for substrates/products, cofactor and drug binding, catalytic sites, and ligand/protein. In addition, channels formed by transmembrane (TM) proteins serve as transporters and ion channels. MolAxis is a new sensitive and fast tool for the identification and classification of channels and cavities of various sizes and shapes in macromolecules. MolAxis constructs corridors, which are pathways that represent probable routes taken by small molecules passing through channels. The outer medial axis of the molecule is the collection of points that have more than one closest atom. It is composed of two-dimensional surface patches and can be seen as a skeleton of the complement of the molecule. We have implemented in MolAxis a novel algorithm that uses state-of-the-art computational geometry techniques to approximate and scan a useful subset of the outer medial axis, thereby reducing the dimension of the problem and consequently rendering the algorithm extremely efficient. MolAxis is designed to identify channels that connect buried cavities to the outside of macromolecules and to identify TM channels in proteins. We apply MolAxis to enzyme cavities and TM proteins. We further utilize MolAxis to monitor channel dimensions along Molecular Dynamics trajectories of a human Cytochrome P450. MolAxis constructs high quality corridors for snapshots at picosecond time-scale intervals substantiating the gating mechanism in the 2e substrate access channel. We compare our results with previous tools in terms of accuracy, performance and underlying theoretical guarantees of finding the desired pathways. MolAxis is available on line as a web-server and as a stand alone easy-to-use program (http://bioinfo3d.cs.tau.ac.il/MolAxis/).

Prediction and simulation of motion in pairs of transmembrane alpha-helices.

MOTIVATION: Motion in transmembrane (TM) proteins plays an essential role in a variety of biological phenomena. Thus, developing an automated method for predicting and simulating motion in this class of proteins should result in an increased level of understanding of crucial physiological mechanisms. We have developed an algorithm for predicting and simulating motion in TM proteins of the alpha-helix bundle type. Our method employs probabilistic motion-planning techniques to suggest possible collision-free motion paths. The resulting paths are ranked according to the quality of the van der Waals interactions between the TM helices. Our algorithm considers a wide range of degrees of freedom (dofs) involved in the motion, including external and internal moves. However, in order to handle the vast dimensionality of the problem, we employ some constraints on these dofs in a way that is unlikely to rule out the native motion of the protein. Our algorithm simulates the motion, including all the dofs, and automatically produces a movie that demonstrates it. RESULTS: Overexpression of the RTK ErbB2 was implicated in causing a variety of human cancers. Recently, a molecular mechanism for rotation-coupled activation of the receptor was suggested. We applied our algorithm to investigate the TM domain of this protein, and compared our results with this mechanism. A motion pathway that was similar to the proposed mechanism ranked first, and motions with partial overlap to this pathway followed in rank order. In addition, we conducted a negative-control computational-experiment using Glycophorin A. Our results confirmed the immobility of this TM protein, resulting in degenerate paths comprising native-like conformations.

Quasi-symmetry in the cryo-EM structure of EmrE provides the key to modeling its transmembrane domain.

Small multidrug resistance (SMR) transporters contribute to bacterial resistance by coupling the efflux of a wide range of toxic aromatic cations, some of which are commonly used as antibiotics and antiseptics, to proton influx. EmrE is a prototypical small multidrug resistance transporter comprising four transmembrane segments (M1-M4) that forms dimers. It was suggested recently that EmrE molecules in the dimer have different topologies, i.e. monomers have opposite orientations with respect to the membrane plane. A 3-D structure of EmrE acquired by electron cryo-microscopy (cryo-EM) at 7.5 Angstroms resolution in the membrane plane showed that parts of the structure are related by quasi-symmetry. We used this symmetry relationship, combined with sequence conservation data, to assign the transmembrane segments in EmrE to the densities seen in the cryo-EM structure. A C alpha model of the transmembrane region was constructed by considering the evolutionary conservation pattern of each helix. The model is validated by much of the biochemical data on EmrE with most of the positions that were identified as affecting substrate translocation being located around the substrate-binding cavity. A suggested mechanism for proton-coupled substrate translocation in small multidrug resistance antiporters provides a mechanistic rationale to the experimentally observed inverted topology.

Assigning transmembrane segments to helices in intermediate-resolution structures.

MOTIVATION: Transmembrane (TM) proteins that form alpha-helix bundles constitute approximately 50% of contemporary drug targets. Yet, it is difficult to determine their high-resolution (< 4 A) structures. Some TM proteins yield more easily to structure determination using cryo electron microscopy (cryo-EM), though this technique most often results in lower resolution structures, precluding an unambiguous assignment of TM amino acid sequences to the helices seen in the structure. We present computational tools for assigning the TM segments in the protein's sequence to the helices seen in cryo-EM structures. RESULTS: The method examines all feasible TM helix assignments and ranks each one based on a score function that was derived from loops in the structures of soluble alpha-helix bundles. A set of the most likely assignments is then suggested. We tested the method on eight TM chains of known structures, such as bacteriorhodopsin and the lactose permease. Our results indicate that many assignments can be rejected at the outset, since they involve the connection of pairs of remotely placed TM helices. The correct assignment received a high score, and was ranked highly among the remaining assignments. For example, in the lactose permease, which contains 12 TM helices, most of which are connected by short loops, only 12 out of 479 million assignments were found to be feasible, and the native one was ranked first. AVAILABILITY: The program and the non-redundant set of protein structures used here are available at http://www.cs.tau.ac.il/~angela

Algorithm and data structures for efficient energy maintenance during Monte Carlo simulation of proteins.

Monte Carlo simulation (MCS) is a common methodology to compute pathways and thermodynamic properties of proteins. A simulation run is a series of random steps in conformation space, each perturbing some degrees of freedom of the molecule. A step is accepted with a probability that depends on the change in value of an energy function. Typical energy functions sum many terms. The most costly ones to compute are contributed by atom pairs closer than some cutoff distance. This paper introduces a new method that speeds up MCS by exploiting the facts that proteins are long kinematic chains and that few degrees of freedom are changed at each step. A novel data structure, called the ChainTree, captures both the kinematics and the shape of a protein at successive levels of detail. It is used to efficiently detect self-collision (steric clash between atoms) and/or find all atom pairs contributing to the energy. It also makes it possible to identify partial energy sums left unchanged by a perturbation, thus allowing the energy value to be incrementally updated. Computational tests on four proteins of sizes ranging from 68 to 755 amino acids show that MCS with the ChainTree method is significantly faster (as much as 10 times faster for the largest protein) than with the widely used grid method. They also indicate that speed-up increases with larger proteins.