Noah C. Benson

Dynameomics: A Comprehensive Database of Protein Dynamics

The dynamic behavior of proteins is important for an understanding of their function and folding. We have performed molecular dynamics simulations of the native state and unfolding pathways of over 2000 protein/peptide systems (approximately 11,000 independent simulations) representing the majority of folds in globular proteins. These data are stored and organized using an innovative database approach, which can be mined to obtain both general and specific information about the dynamics and folding/unfolding of proteins, relevant subsets thereof, and individual proteins. Here we describe the project in general terms and the type of information contained in the database. Then we provide examples of mining the database for information relevant to protein folding, structure building, the effect of single-nucleotide polymorphisms, and drug design. The native state simulation data and corresponding analyses for the 100 most populated metafolds, together with related resources, are publicly accessible through http://www.dynameomics.org.

The Retinotopic Organization of Striate Cortex Is Well Predicted by Surface Topology

In 1918, Gordon Holmes combined observations of visual-field scotomas across brain-lesioned soldiers to produce a schematic map of the projection of the visual field upon the striate cortex. One limit to the precision of his result, and the mapping of anatomy to retinotopy generally, is the substantial individual variation in the size, volumetric position, and cortical magnification of area V1. When viewed within the context of the curvature of the cortical surface, however, the boundaries of striate cortex fall at a consistent location across individuals. We asked whether the surface topology of the human brain can be used to accurately predict the internal, retinotopic function of striate cortex as well. We used fMRI to measure polar angle and eccentricity in 25 participants and combined their maps within a left-right, transform-symmetric representation of the cortical surface. These data were then fit using a deterministic, algebraic model of visual-field representation. We found that an anatomical image alone can be used to predict the retinotopic organization of striate cortex for an individual with accuracy equivalent to 10-25 min of functional mapping. This indicates tight developmental linkage of structure and function within a primary, sensory cortical area.

Dynameomics: Large‐scale assessment of native protein flexibility

Structure is only the first step in understanding the interactions and functions of proteins. In this paper, we explore the flexibility of proteins across a broad database of over 250 solvated protein molecular dynamics simulations in water for an aggregate simulation time of ∼6 μs. These simulations are from our Dynameomics project, and these proteins represent approximately 75% of all known protein structures. We employ principal component analysis of the atomic coordinates over time to determine the primary axis and magnitude of the flexibility of each atom in a simulation. This technique gives us both a database of flexibility for many protein fold families and a compact visual representation of a particular proteins native‐state conformational space, neither of which are available using experimental methods alone. These tools allow us to better understand the nature of protein motion and to describe its relationship to other structural and dynamical characteristics. In addition to reporting general properties of protein flexibility and detailing many dynamic motifs, we characterize the relationship between protein native‐state flexibility and early events in thermal unfolding and show that flexibility predicts how a protein will begin to unfold. We provide evidence that fold families have conserved flexibility patterns, and family members who deviate from the conserved patterns have very low sequence identity. Finally, we examine novel aspects of highly inflexible loops that are as important to structural integrity as conventional secondary structure. These loops, which are difficult if not impossible to locate without dynamic data, may constitute new structural motifs.

Dynameomics: design of a computational lab workflow and scientific data repository for protein simulations.

Dynameomics is a project to investigate and catalog the native-state dynamics and thermal unfolding pathways of representatives of all protein folds using solvated molecular dynamics simulations, as described in the preceding paper. Here we introduce the design of the molecular dynamics data warehouse, a scalable, reliable repository that houses simulation data that vastly simplifies management and access. In the succeeding paper, we describe the development of a complementary multidimensional database. A single protein unfolding or native-state simulation can take weeks to months to complete, and produces gigabytes of coordinate and analysis data. Mining information from over 3000 completed simulations is complicated and time-consuming. Even the simplest queries involve writing intricate programs that must be built from low-level file system access primitives and include significant logic to correctly locate and parse data of interest. As a result, programs to answer questions that require data from hundreds of simulations are very difficult to write. Thus, organization and access to simulation data have been major obstacles to the discovery of new knowledge in the Dynameomics project. This repository is used internally and is the foundation of the Dynameomics portal site http://www.dynameomics.org. By organizing simulation data into a scalable, manageable and accessible form, we can begin to address substantial questions that move us closer to solving biomedical and bioengineering problems.

Correction of distortion in flattened representations of the cortical surface allows prediction of V1-V3 functional organization from anatomy.

Several domains of neuroscience offer map-like models that link location on the cortical surface to properties of sensory representation. Within cortical visual areas V1, V2, and V3, algebraic transformations can relate position in the visual field to the retinotopic representation on the flattened cortical sheet. A limit to the practical application of this structure-function model is that the cortex, while topologically a two-dimensional surface, is curved. Flattening of the curved surface to a plane unavoidably introduces local geometric distortions that are not accounted for in idealized models. Here, we show that this limitation is overcome by correcting the geometric distortion induced by cortical flattening. We use a mass-spring-damper simulation to create a registration between functional MRI retinotopic mapping data of visual areas V1, V2, and V3 and an algebraic model of retinotopy. This registration is then applied to the flattened cortical surface anatomy to create an anatomical template that is linked to the algebraic retinotopic model. This registered cortical template can be used to accurately predict the location and retinotopic organization of these early visual areas from cortical anatomy alone. Moreover, we show that prediction accuracy remains when extrapolating beyond the range of data used to inform the model, indicating that the registration reflects the retinotopic organization of visual cortex. We provide code for the mass-spring-damper technique, which has general utility for the registration of cortical structure and function beyond the visual cortex.

The Fine-Scale Functional Correlation of Striate Cortex in Sighted and Blind People

To what extent are spontaneous neural signals within striate cortex organized by vision? We examined the fine-scale pattern of striate cortex correlations within and between hemispheres in rest-state BOLD fMRI data from sighted and blind people. In the sighted, we find that corticocortico correlation is well modeled as a Gaussian point-spread function across millimeters of striate cortical surface, rather than degrees of visual angle. Blindness produces a subtle change in the pattern of fine-scale striate correlations between hemispheres. Across participants blind before the age of 18, the degree of pattern alteration covaries with the strength of long-range correlation between left striate cortex and Brocas area. This suggests that early blindness exchanges local, vision-driven pattern synchrony of the striate cortices for long-range functional correlations potentially related to cross-modal representation.

Resting-state retinotopic organization in the absence of retinal input and visual experience

Early visual areas have neuronal receptive fields that form a sampling mosaic of visual space, resulting in a series of retinotopic maps in which the same region of space is represented in multiple visual areas. It is not clear to what extent the development and maintenance of this retinotopic organization in humans depend on retinal waves and/or visual experience. We examined the corticocortical receptive field organization of resting-state BOLD data in normally sighted, early blind, and anophthalmic (in which both eyes fail to develop) individuals and found that resting-state correlations between V1 and V2/V3 were retinotopically organized for all subject groups. These results show that the gross retinotopic pattern of resting-state connectivity across V1-V3 requires neither retinal waves nor visual experience to develop and persist into adulthood. SIGNIFICANCE STATEMENT Evidence from resting-state BOLD data suggests that the connections between early visual areas develop and are maintained even in the absence of retinal waves and visual experience.

A comparison of multiscale methods for the analysis of molecular dynamics simulations.

Molecular dynamics (MD) is the only technique available for obtaining dynamic protein data at atomic spatial resolution and picosecond or finer temporal resolution. In recent years, the cost of computational resources has decreased exponentially while the number of known protein structures, many of which are not characterized biochemically, has increased rapidly. These events have led to an increase in the use of MD in biological research, both to examine phenomena that cannot be resolved experimentally and to generate hypotheses that direct further experimental research. In fact, several databases of MD simulations have arisen in recent years. MD simulations, and especially MD simulation databases, contain massive amounts of data, yet interesting phenomena often occur over very short time periods and on the scale of only a few atoms. Analysis of such data must balance these fine-detail events with the global picture they create. Here, we address the multiscale nature of the problem by comparing several MD analysis methods to show their strengths and weaknesses at various scales using the wild-type and R282W mutant forms of the DNA-binding domain of protein p53. By leveraging these techniques together, we are able to pinpoint fine-detail and big picture differences between the proteins variants. Our analyses indicate that the R282W mutation of p53 destabilizes the L1 loop and loosens the H2 helix conformation, but the loosened L1 loop can be rescued by residue H115, preventing the R282W mutation from completely destabilizing the protein or abolishing activity.

Hierarchical and homotopic correlations of spontaneous neural activity within the visual cortex of the sighted and blind

Spontaneous neural activity within visual cortex is synchronized by both monosynaptic, hierarchical connections between visual areas and indirect, network-level activity. We examined the interplay of hierarchical and network connectivity in human visual cortex by measuring the organization of spontaneous neural signals within the visual cortex in total darkness using functional magnetic resonance imaging (fMRI). Twenty-five blind (14 congenital and 11 postnatal) participants with equally severe vision loss and 22 sighted subjects were studied. An anatomical template based on cortical surface topology was used for all subjects to identify the quarter-field components of visual areas V1-V3, and assign retinotopic organization. Cortical visual areas that represent the same quadrant of the visual field were considered to have a hierarchical relationship, while the spatially separated quarters of the same visual area were considered homotopic. Blindness was found to enhance correlations between hierarchical cortical areas as compared to indirect, homotopic areas at both the level of visual areas (p = 0.000031) and fine, retinotopic scale (p = 0.0024). A specific effect of congenital, but not postnatal, blindness was to further broaden the cortico-cortico connections between hierarchical visual areas (p = 0.0029). This finding is consistent with animal studies that observe a broadening of axonal terminal arborization when the visual cortex is deprived of early input. We therefore find separable roles for vision in developing and maintaining the intrinsic neural activity of visual cortex.

A CHEMICAL GROUP GRAPH REPRESENTATION FOR EFFICIENT HIGH-THROUGHPUT ANALYSIS OF ATOMISTIC PROTEIN SIMULATIONS

Graphs are rapidly becoming a powerful and ubiquitous tool for the analysis of protein structure and for event detection in dynamical protein systems. Despite their rise in popularity, however, the graph representations employed to date have shared certain features and parameters that have not been thoroughly investigated. Here, we examine and compare variations on the construction of graph nodes and graph edges. We propose a graph representation based on chemical groups of similar atoms within a protein rather than residues or secondary structure and find that even very simple analyses using this representation form a powerful event detection system with significant advantages over residue-based graph representations. We additionally compare graph edges based on probability of contact to graph edges based on contact strength and analyses of the entire graph structure to an alternative and more computationally tractable node-based analysis. We develop the simplest useful technique for analyzing protein simulations based on these comparisons and use it to shed light on the speed with which static protein structures adjust to a solvated environment at room temperature in simulation.