Computational identification of self-inhibitory peptides from envelope proteins

Fusion process is known to be the initial step of viral infection and hence targeting the entry process is a promising strategy to design antiviral therapy. The self-inhibitory peptides derived from the enveloped (E) proteins function to inhibit the proteinprotein interactions in the membrane fusion step mediated by the viral E protein. Thus, they have the potential to be developed into effective antiviral therapy. Herein, we have developed a Monte Carlo-based computational method with the aim to identify and optimize potential peptide hits from the E proteins. The stability of the peptides, which indicates their potential to bind in situ to the E proteins, was evaluated by two different scoring functions, dipolar distance-scaled, finite, ideal-gas reference state and residue-specific all-atom probability discriminatory function. The method was applied to a-helical Class I HIV-1 gp41, beta-sheet Class II Dengue virus (DENV) type 2 E proteins, as well as Class III Herpes Simplex virus-1 (HSV-1) glycoprotein, a E protein with a mixture of a-helix and beta-sheet structural fold. The peptide hits identified are in line with the druggable regions where the self-inhibitory peptide inhibitors for the three classes of viral fusion proteins were derived. Several novel peptides were identified from either the hydrophobic regions or the functionally important regions on Class II DENV-2 E protein and Class III HSV-1 gB. They have potential to disrupt the proteinprotein interaction in the fusion process and may serve as starting points for the development of novel inhibitors for viral E proteins. 

Molecular Computational Elements Encode Large Populations of Small Objects

Since the introduction of molecular computation1, 2, experimental molecular computational elements have grown3, 4, 5 to encompass small-scale integration6, arithmetic7 and games8, among others. However, the need for a practical application has been pressing. Here we present molecular computational identification (MCID), a demonstration that molecular logic and computation can be applied to a widely relevant issue. Examples of populations that need encoding in the microscopic world are cells in diagnostics or beads in combinatorial chemistry (tags). Taking advantage of the small size9 (about 1 nm) and large 'on/off' output ratios of molecular logic gates and using the great variety of logic types, input chemical combinations, switching thresholds and even gate arrays in addition to colours, we produce unique identifiers for members of populations of small polymer beads (about 100 m) used for synthesis of combinatorial libraries10, 11. Many millions of distinguishable tags become available. This method should be extensible to far smaller objects, with the only requirement being a 'wash and watch' protocol12. Our focus on converting molecular science into technology concerning analog sensors13, 14, turns to digital logic devices in the present work.

Solid-bound, proton-driven, fluorescent ‘off–on–off’ switches based on PET (photoinduced electron transfer)

Anthracene-based, H+-driven, ‘off–on–off’ fluorescent PET (photoinduced electron transfer) switches are immobilized on organic and inorganic polymeric solids in the form of Tentagel® and silica, respectively. The environment of the organic bead displaces apparent switching thresholds towards lower pH values whereas the Si–O- groups of silica electrostatically cause the opposite effect. These switches are ternary logic gate tags, one of which can be particularly useful in strengthening molecular computational identification (MCID) of small solid objects.

Molecular Logic Gate Arrays

Chemists are now able to emulate the ideas and instruments of mathematics and computer science with molecules. The integration of molecular logic gates into small arrays has been a growth area during the last few years. The design principles underlying a collection of these cases are examined. Some of these computing molecules are applicable in medical- and biotechnologies. Cases of blood diagnostics, 'lab-on-a-molecule' systems, and molecular computational identification of small objects are included.

2010: A Small Space Odyssey with Luminescent Molecules

The road to molecular logic and computation in Belfast, Northern Ireland started with chemical sensors in Colombo, Sri Lanka. This journey is mapped out with reference to design principles, such as those for luminescent PET (photoinduced electron transfer) sensing. Applications such as those for blood electrolyte diagnostics, "lab-on-a-molecule" systems, and molecular computational identification (MCID) are also met along the way.

From Sensing to Molecular Logic: A Journey

A personal account of the establishment of luminescent PET (photoinduced electron transfer) sensing and its development into molecular logic is given. Several applications of these two research areas, e.g. blood electrolyte diagnostics, ‘lab-on-amolecule’ systems and molecular computational identification (MCID) are illustrated.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.