Vinay Pulim

Local topology of silica networks

Model-building algorithms based on self-assembly according to local rules have been devised, which permit rapid assembly of vertex-sharing tetrahedral network structures. These have been applied to investigation of the topological properties of crystalline and amorphous network silicas. Local assembly rules for the six compact crystalline silica tetrahedral network polymorphs have been formulated which reproduce the fundamental topologies of the polymorphs and permit investigation of the range of displacive modifications. Amorphous networks can be generated by application of deviant rules, and two are explored on the basis of modifications of quartz and cristobalite assembly rules. The global topologies of the crystalline polymorphs are found to be fully embodied in their local clusters, which consists of sets of irreducible rings and associated tetrahedra. The local clusters provide a local description of structure that serves as an alternative to the unit cell of crystallography and is applicable to non-periodic structures. The availability of the full adjacency matrix and tetrahedron coordinates, for generated crystalline and amorphous assemblages alike, provides ready access to all local clusters, bond-angle distributions and partial radial correlations.

Optimal contact map alignment of protein–protein interfaces

The long-standing problem of constructing protein structure alignments is of central importance in computational biology. The main goal is to provide an alignment of residue correspondences, in order to identify homologous residues across chains. A critical next step of this is the alignment of protein complexes and their interfaces. Here, we introduce the program CMAPi, a two-dimensional dynamic programming algorithm that, given a pair of protein complexes, optimally aligns the contact maps of their interfaces: it produces polynomial-time near-optimal alignments in the case of multiple complexes. We demonstrate the efficacy of our algorithm on complexes from PPI families listed in the SCOPPI database and from highly divergent cytokine families. In comparison to existing techniques, CMAPi generates more accurate alignments of interacting residues within families of interacting proteins, especially for sequences with low similarity. While previous methods that use an all-atom based representation of the interface have been successful, CMAPis use of a contact map representation allows it to be more tolerant to conformational changes and thus to align more of the interaction surface. These improved interface alignments should enhance homology modeling and threading methods for predicting PPIs by providing a basis for generating template profiles for sequence–structure alignment. Contact: bab@mit.edu; jbienkowska@gmail.com Supplementary information: Supplementary data are available at http://theory.csail.mit.edu/cmapi

Modeling the cascade amorphization of silicas with local-rules reconstruction

Abstract Displacement-cascade disorder was modeled topologically in two crystalline SiO2 polymorphs (quartz, cristobalite) and one amorphous silica in an attempt to understand the origins of the experimentally observed common terminal density after prolonged irradiation. Cascades were simulated by imposing random rotations of constituent [SiO4] tetrahedra followed by reconnection according to several sets of local assembly rules derived previously for crystalline or non-crystalline assemblages. The resulting arrangements were characterized by their bond-angle distributions, ring topology of their local clusters, and pair correlations. It is concluded from their similarity that fundamental topological constraints underlie the commonalities of the endpoint configurations.

Topological modeling of amorphized tetrahedral ceramic network structures

Abstract The topology of the tetrahedral network compounds SiO 2 , Si 3 N 4 and SiC is considered and applied to the potential for amorphization of these compounds. Local rules are devised for self-assembly modeling of crystalline polymorphs of the latter two, similar to those derived previously for SiO 2 , and modifications of these rules are applied to generate topologically disordered versions. Success in obtaining viable amorphous networks is assessed in terms of residual underconnection, tetrahedral distortion, partial radial correlations, and local clusters of tetrahedra that embody the local topology. Topologically assembled models of amorphous Si 3 N 4 are found to be constructible, but with radial correlations and local clusters much closer to those of the parent crystals than for assembled amorphous models of silica. Models of amorphous SiC remain substantially underconnected and highly distorted tetrahedrally; the seemingly contrary experimentally observed facile amorphization of SiC under irradiation is suggested to be facilitated by the potential for chemical disorder in SiC that alters the topological possibilities.

Topological modeling of cascade amorphization in network structures using local rules

Abstract A local rules-based assembly procedure was used to erect large periodic and aperiodic models of the tetrahedral network compounds SiO 2 , Si 3 N 4 and SiC, whose topological properties were assessed in a prior investigation. Several hundred tetrahedra in central regions of the models were disconnected and randomly rotated, then allowed to reconnect according to an imposed set of the same or different rules, in order to simulate radiation disordering within a dense collision cascade. Alterations in the rebonded cascade region were characterized by assessment of density, underconnection, tetrahedral distortion, inter-tetrahedral bond angle, radial correlations and local topology. While SiO 2 networks amorphize easily and acceptably in this way, Si 3 N 4 and SiC networks do not. It is concluded that the experimental difficulty in amorphizing Si 3 N 4 is due to the topological difficulty in tetrahedral reconnection from random orientations according to the applicable local rules for Si 3 N 4 , while the observed ease of amorphizing SiC must arise from the potential for chemical disorder in that compound.

LTHREADER : Prediction of extracellular ligand-receptor interactions in cytokines using localized threading

Identification of extracellular ligand–receptor interactions is important for drug design and the treatment of diseases. Difficulties in detecting these interactions using high‐throughput experimental techniques motivate the development of computational prediction methods. We propose a novel threading algorithm, LTHREADER, which generates accurate local sequence‐structure interface alignments and integrates various statistical scores and experimental binding data to predict interactions within ligand–receptor families. LTHREADER uses a profile of secondary structure and solvent accessibility predictions with residue contact maps to guide and constrain alignments. Using a decision tree classifier and low‐throughput experimental data for training, it combines information inferred from statistical interaction potentials, energy functions, correlated mutations, and conserved residue pairs to predict interactions. We apply our method to cytokines, which play a central role in the development of many diseases including cancer and inflammatory and autoimmune disorders. We tested our approach on two representative families from different structural classes (all‐α and all‐β proteins) of cytokines. In comparison with the state‐of‐the‐art threader RAPTOR, LTHREADER generates on average 20% more accurate alignments of interacting residues. Furthermore, in cross‐validation tests, LTHREADER correctly predicts experimentally confirmed interactions for a common binding mode within the 4‐helical long‐chain cytokine family with 75% sensitivity and 86% specificity with 40% gain in sensitivity compared to RAPTOR. For the TNF‐like family our method achieves 70% sensitivity with 55% specificity with 70% gain in sensitivity. LTHREADER combines information from multiple complex templates when such data are available. When only one solved structure is available, a localized PSI‐BLAST approach also outperforms standard threading methods with 25%–50% improvements in sensitivity.

Molecular dynamics refinement of topologically generated reconstructions of simulated irradiation cascades in silica networks

Silica irradiation cascade structures, simulated using topological modeling approaches, have been refined using molecular dynamics simulation techniques. Major structural reconstruction was observed when silica is equilibrated in this way at and above a glass transition temperature. Below the glass transition, irradiated cascades were found to have largely retained their original topological structures, and in this way several fully connected metastable silicas with substantially different medium-range structures were obtained. Analysis of these structures further revealed that their total correlation functions were remarkably insensitive to changes in their medium-range ring complement. Information in the first sharp diffraction peak (FSDP) is shown instead to provide some insight into the topologies of irradiated silicas.

The nanostructures of amorphous silicas.

Topologically modeled amorphized silica structures have been refined using a molecular dynamics simulation technique. Several metastable structures with substantially different medium-range connectivities, as characterized by primitive ring statistics, were obtained. Whereas the total correlation function is insensitive to these differences, the first step diffraction peak derived from energy-filtered electron diffraction shows a promising correlation to medium-range structure.

LTHREADER: prediction of ligand-receptor interactions using localized threading.

Identification of ligand-receptor interactions is important for drug design and treatment of diseases. Difficulties in detecting these interactions using high-throughput experimental techniques motivate the development of computational prediction methods. We propose a novel threading algorithm, LTHREADER, which generates accurate local sequence-structure alignments and integrates statistical and energy scores to predict interactions within ligand-receptor families. LTHREADER uses a profile of secondary structure and solvent accessibility predictions with residue contact maps to guide and constrain alignments. Using a decision tree classifier and low-throughput experimental data for training, it combines information inferred from statistical interaction potentials, energy functions, correlated mutations and conserved residue pairs to predict likely interactions. The significance of predicted interactions is evaluated using the scores for randomized binding surfaces within each family. We apply our method to cytokines, which play a central role in the development of many diseases including cancer and inflammatory and autoimmune disorders. We tested our approach on two representatives from different structural classes (all-alpha and all-beta proteins) of cytokines. In comparison with state-of-the-art threader, RAPTOR, LTHREADER generates on average 20% more accurate alignments of interacting residues. Furthermore, in cross-validation tests, LTHREADER correctly predicts experimentally confirmed interactions for a common binding mode within the 4-helical long chain cytokine family with 75% sensitivity and 86% specificity. For the TNF-like family our method achieves 70% sensitivity with 55% specificity. This is a dramatic improvement over existing methods. Moreover, LTHREADER predicts several novel potential ligand-receptor cytokine interactions.

MD Refinement of Topologically Simulated Irradiation Cascades in Silica

Refinement of several topologically generated displacement cascades in silica has been conducted using molecular dynamics (MD) simulation. Several metastable amorphous silicas with substantially different medium-range order (as characterized by ring topologies) were obtained. However, their total correlation functions were found scarcely distinguishable. Major structural reconstruction was observed when the refinement took place above a glass transition temperature, below which the cascades largely retained their original topological ring structures. Attempts are made to correlate topological ring distributions with the first sharp diffraction peak, which may in turn provide some insight into the medium range structures of irradiated silicas.