Shruthi Viswanath

Improving ranking of models for protein complexes with side chain modeling and atomic potentials

An atomically detailed potential for docking pairs of proteins is derived using mathematical programming. A refinement algorithm that builds atomically detailed models of the complex and combines coarse grained and atomic scoring is introduced. The refinement step consists of remodeling the interface side chains of the top scoring decoys from rigid docking followed by a short energy minimization. The refined models are then re‐ranked using a combination of coarse grained and atomic potentials. The docking algorithm including the refinement and re‐ranking, is compared favorably to other leading docking packages like ZDOCK, Cluspro, and PATCHDOCK, on the ZLAB 3.0 Benchmark and a test set of 30 novel complexes. A detailed analysis shows that coarse grained potentials perform better than atomic potentials for realistic unbound docking (where the exact structures of the individual bound proteins are unknown), probably because atomic potentials are more sensitive to local errors. Nevertheless, the atomic potential captures a different signal from the residue potential and as a result a combination of the two scores provides a significantly better prediction than each of the approaches alone. Proteins 2013.

DOCK/PIERR: web server for structure prediction of protein-protein complexes.

In protein docking we aim to find the structure of the complex formed when two proteins interact. Protein-protein interactions are crucial for cell function. Here we discuss the usage of DOCK/PIERR. In DOCK/PIERR, a uniformly discrete sampling of orientations of one protein with respect to the other, are scored, followed by clustering, refinement, and reranking of structures. The novelty of this method lies in the scoring functions used. These are obtained by examining hundreds of millions of correctly and incorrectly docked structures, using an algorithm based on mathematical programming, with provable convergence properties.

Analyzing milestoning networks for molecular kinetics: definitions, algorithms, and examples.

Network representations are becoming increasingly popular for analyzing kinetic data from techniques like Milestoning, Markov State Models, and Transition Path Theory. Mapping continuous phase space trajectories into a relatively small number of discrete states helps in visualization of the data and in dissecting complex dynamics to concrete mechanisms. However, not only are molecular networks derived from molecular dynamics simulations growing in number, they are also getting increasingly complex, owing partly to the growth in computer power that allows us to generate longer and better converged trajectories. The increased complexity of the networks makes simple interpretation and qualitative insight of the molecular systems more difficult to achieve. In this paper, we focus on various network representations of kinetic data and algorithms to identify important edges and pathways in these networks. The kinetic data can be local and partial (such as the value of rate coefficients between states) or an exact solution to kinetic equations for the entire system (such as the stationary flux between vertices). In particular, we focus on the Milestoning method that provides fluxes as the main output. We proposed Global Maximum Weight Pathways as a useful tool for analyzing molecular mechanism in Milestoning networks. A closely related definition was made in the context of Transition Path Theory. We consider three algorithms to find Global Maximum Weight Pathways: Recursive Dijkstras, Edge-Elimination, and Edge-List Bisection. The asymptotic efficiency of the algorithms is analyzed and numerical tests on finite networks show that Edge-List Bisection and Recursive Dijkstras algorithms are most efficient for sparse and dense networks, respectively. Pathways are illustrated for two examples: helix unfolding and membrane permeation. Finally, we illustrate that networks based on local kinetic information can lead to incorrect interpretation of molecular mechanisms.

The molecular architecture of the yeast spindle pole body core determined by Bayesian integrative modeling

A model of the core of the yeast spindle pole body (SPB) was created by a Bayesian modeling approach that integrated a diverse data set of biophysical, biochemical, and genetic information. The model led to a proposed pathway for the assembly of Spc110, a protein related to pericentrin, and a mechanism for how calmodulin strengthens the SPB during mitosis.

Integrative structure modeling with the Integrative Modeling Platform

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.

Extension of a protein docking algorithm to membranes and applications to amyloid precursor protein dimerization

Novel adjustments are introduced to the docking algorithm, DOCK/PIERR, for the purpose of predicting structures of transmembrane protein complexes. Incorporating knowledge about the membrane environment is shown to significantly improve docking accuracy. The extended version of DOCK/PIERR is shown to perform comparably to other leading docking packages. This membrane version of DOCK/PIERR is applied to the prediction of coiled‐coil homodimer structures of the transmembrane region of the C‐terminal peptide of amyloid precursor protein (C99). Results from MD simulation of the C99 homodimer in POPC bilayer and docking are compared. Docking results are found to capture key aspects of the homodimer ensemble, including the existence of three topologically distinct conformers. Furthermore, the extended version of DOCK/PIERR is successful in capturing the effects of solvation in membrane and micelle. Specifically, DOCK/PIERR reproduces essential differences in the homodimer ensembles simulated in POPC bilayer and DPC micelle, where configurational entropy and surface curvature effects bias the handedness and topology of the homodimer ensemble. Proteins 2015; 83:2170–2185.

Color call improvement in next generation sequencing using multi-class support vector machines

Background There is considerable ongoing effort towards making DNA sequencing machines faster and more affordable today. Improving the accuracy of next-generation sequencers directly lowers sequencing costs by reducing the need for resequencing, making genome-based diagnostics and research more affordable [1]. In this paper, we show how the accuracy of next-generation sequencing machines is significantly improved using supervised learning, specifically, multi-class support vector machines. We demonstrate our methods on the SOLiD 5500/5500 XL platform. Base-calling is the process of determining the order of nucleotides in the read sequence. In SOLiD, base-calling involves the process of color calling, since the SOLiD platform uses an encoding system where each adjacent pair of nucleotides is represented by one of four colored dyes [2]. Base-callers have been developed for other nextgeneration sequencing platforms, in particular Illumina and Roche 454 [1]. Most of them are based on explicit statistical models and some are based on support vector based supervised learning [3,4]. But ours is the first supervised learning method applied on a large scale directly to color space. Also, this is the first supervised learning method to be applied on a large-scale to SOLiD. Moreover, we show that our methods require less training data and hence our training times are much faster than previous methods.

Integrative structure modeling with the Integrative Modeling Platform: Integrative Structure Modeling with IMP

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.

Spc110 N-Terminal Domains Act Independently to Mediate Stable γ-Tubulin Small Complex Binding and γ-Tubulin Ring Complex Assembly

Microtubule (MT) nucleation in vivo is regulated by the γ-tubulin ring complex (γTuRC), an approximately 2-megadalton complex conserved from yeast to humans. In Saccharomyces cerevisiae, γTuRC assembly is a key point of regulation over the MT cytoskeleton. Budding yeast γTuRC is composed of seven γ-tubulin small complex (γTuSC) subassemblies which associate helically to form a template from which microtubules grow. This assembly process requires higher-order oligomers of the coiled-coil protein Spc110 to bind multiple γTuSCs, thereby stabilizing the otherwise low-affinity interface between γTuSCs. While Spc110 oligomerization is critical, its N-terminal domain (NTD) also plays a role that is poorly understood both functionally and structurally. In this work, we sought a mechanistic understanding of Spc110 NTD using a combination of structural and biochemical analyses. Through crosslinking-mass spectrometry (XL-MS), we determined that a segment of Spc110 coiled-coil is a major point of contact with γTuSC. We determined the structure of this coiled-coil segment by X-ray crystallography and used it in combination with our XL-MS dataset to generate an integrative structural model of the γTuSC-Spc110 complex. This structural model, in combination with biochemical analyses of Spc110 heterodimers lacking one NTD, suggests that the two NTDs within an Spc110 dimer act independently, one stabilizing association between Spc110 and γTuSC and the other stabilizing the interface between adjacent γTuSCs.

Integrative structure modeling with IMP

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.