arXiv subjects

Aysegul Ozkan

Publications and source records attributed to Aysegul Ozkan.

6 recordsLinked to original sources

arXiv2023

TURead: An eye movement dataset of Turkish reading

In this study, we present TURead, an eye movement dataset of silent and oral sentence reading in Turkish, an agglutinative language with a shallow orthography understudied in reading research. TURead provides empirical data to investigate the relationship between morphology and oculomotor control. We employ a target-word approach in which target words are manipulated by word length and by the addition of two commonly used suffixes in Turkish. The dataset contains well-established eye movement variables; prelexical characteristics such as vowel harmony and bigram-trigram frequencies and word features, such as word length, predictability, frequency, eye voice span measures, Cloze test scores of the root word and suffix predictabilities, as well as the scores obtained from two working memory tests. Our findings on fixation parameters and word characteristics are in line with the patterns reported in the relevant literature.

cs.CL↗

arXiv2020

Atlasing of Assembly Landscapes using Distance Geometry and Graph Rigidity

We describe a novel geometric methodology for analyzing free-energy and kinetics of assembly driven by short-range pair-potentials in an implicit solvent, and provides illustrations of its unique capabilities. An atlas is a labeled partition of the assembly landscape into a topological roadmap of maximal, contiguous, nearly-equipotential-energy conformational regions or macrostates, together with their neighborhood relationships. The new methodology decouples the roadmap generation from sampling and produces: (1) a query-able atlas of local potential energy minima, their basin structure, energy barriers, and neighboring basins; (2) paths between a specified pair of basins; and (3) approximations of relative path lengths, basin volumes (configurational entropy), and path probabilities. Results demonstrating the core algorithm's capabilities have been generated by a resource-light, opensource software implementation EASAL. EASAL atlases several hundred thousand macrostates in minutes on a standard laptop. Subsequent path and basin computations each take seconds. The core algorithm's correctness, time complexity, and efficiency-accuracy tradeoffs are formally guaranteed using modern geometric constraint systems. The methodology further links geometric variables of the input assembling units to a type of intuitive topological bar-code of the output atlas, which in turn determine stable assembled structures and kinetics. This succinct input-output relationship facilitates reverse analysis, and control towards design. We use the novel convex Cayley (distance-based) parametrization that is unique to assembly, as opposed to folding. Sampling microstates with macrostate-specific Cayley parameters avoids gradient-descent search used by all prevailing methods. This increases sampling efficiency, significantly reduces the number of repeated and discarded samples.

cs.CG↗

arXiv2020

Rapid prediction of crucial hotspot interactions for icosahedral viral capsid self-assembly by energy landscape atlasing validated by mutagenesis

Icosahedral viruses have their infectious genome encapsulated by a shell assembled by a multiscale process, starting from an integer multiple of 60 viral capsid or coat protein (VP) monomers. We predict and validate inter-atomic hotspot interactions between VP monomers that are important for the assembly of 3 icosahedral viral capsids: Adeno Associated Virus serotype 2 (AAV2) and Minute Virus of Mice (MVM), both T=1 single stranded DNA viruses, and Bromo Mosaic Virus (BMV), a T=3 single stranded RNA virus. Experimental validation is by in-vitro, site-directed mutagenesis data found in literature. We combine ab-initio predictions at two scales: at the interface-scale, we predict the importance (cruciality) of an interaction for successful subassembly across each interface between VP monomers; and at the capsid-scale, we predict the cruciality of an interface for successful capsid assembly. At the interface-scale, we measure cruciality by changes in the capsid free-energy landscape partition function when an interaction is removed. The partition function computation uses atlases of interface subassembly landscapes, rapidly generated by a novel geometric method and curated opensource software EASAL (efficient atlasing and search of assembly landscapes). At the capsid-scale, cruciality of an interface for successful assembly of the capsid is based on combinatorial entropy. Our study goes from resource-light, multiscale computational predictions of crucial hotspot inter-atomic interactions to validation using data on site-directed mutagenesis' effect on capsid assembly. By reliably and rapidly narrowing down target interactions, (no more than 1.5 hours per interface on a laptop with Intel Core i5-2500K 3.2Ghz CPU and 8GB of RAM) our predictions can inform and reduce time-consuming in-vitro and in-vivo experiments, or more computationally intensive in-silico analyses.

q-bio.BM↗

arXiv2020

Fast and Flexible Geometric Method For Enhancing MC Sampling of Compact Configurations For Protein Docking Problem

EASAL (Efficient Atlasing and Sampling of Assembly Landscapes) is a geometric method for sampling and computing integrals over the potential energy landscape of small molecular assemblies. EASAL's efficiency arises from the fact that small assembly landscapes permit the use of so-called Cayley (inter-atomic distance based) parameters for geometric representation and sampling of the assembly configuration space regions; this results in their isolation, convexification, customized sampling and systematic traversal using a comprehensive topological roadmap. We define custom-designed measurements to investigate and compare various sampling characteristics of EASAL and the traditional Monte Carlo (MC) sampling, including (i) sampling speed, (ii) efficiency and accuracy of uniform grid coverage, (iii) accuracy of weighted coverage at covering low energy regions, (iv) ability to localize sampling to macrostates, and (v) flexibility in sampling distributions. In particular, we compare the sampling characteristics of EASAL and MC in sampling the assembly landscape of 2 trans-membrane helices, with short-range pair-potentials. We demonstrate that EASAL provides a reasonable coverage of crucial but narrow regions of the energy landscape of low effective dimension, with much fewer samples and computational resources than MC sampling. Promising avenues for combining the complementary advantages of the two methods are discussed.

physics.chem-ph↗

arXiv2018

Efficient Atlasing and Search of Configuration Spaces of Point-Sets Constrained by Distance Intervals

For configurations of point-sets that are pairwise constrained by distance intervals, the EASAL software implements a suite of algorithms that characterize the structure and geometric properties of the configuration space. The algorithms generate, describe and explore these configuration spaces using generic rigidity properties, classical results for stratification of semi-algebraic sets, and new results for efficient sampling by convex parametrization. The paper reviews the key theoretical underpinnings, major algorithms and their implementation. The paper outlines the main applications such as the computation of free energy and kinetics of assembly of supramolecular structures or of clusters in colloidal and soft materials. In addition, the paper surveys select experimental results and comparisons.

cs.CG↗

arXiv2014

Best of Both Worlds: Uniform sampling in Cartesian and Cayley Molecular Assembly Configuration Space

EASAL (efficient atlasing and sampling of assembly landscapes) is a recently reported geometric method for representing, visualizing, sampling and computing integrals over the potential energy landscape tailored for small molecular assemblies. EASAL's efficiency arises from the fact that small assembly landscapes permit the use of so-called Cayley parameters (inter-atomic distances) for geometric representation and sampling of the assembly configuration space regions; this results in their isolation, convexification, customized sampling and systematic traversal using a comprehensive topological roadmap, ensuring reasonable coverage of crucial but narrow regions of low effective dimension. However, this alone is inadequate for accurate computation of configurational entropy and other integrals, required for estimation of both free energy and kinetics - where it is essential to obtain uniform sampling in appropriate cartesian or moduli space parameterization. Standard adjustment of Cayley sampling via the Jacobian of the map between the two parameterizations is fraught with challenges stemming from an illconditioned Jacobian. This paper formalizes and analyzes these challenges to provide modifications to EASAL that secure the advantages of Cayley sampling while ensuring certain minimum distance and coverage relationships between sampled configurations - in Cartesian space. The modified EASAL's performance is compared with the basic EASAL and the data are presented for Human and Rat Islet Amylin Polypeptide (HiAPP, PDB-2KJ7 and RiAPP PDB-2KB8) dimerization (the two differ in only 6 out of 37 residues, but the former aggregates into fibrils, while the latter does not).

physics.chem-ph↗