The evaluation of protein folding rate constant is improved by predicting the folding kinetic order with a SVM-based method

Protein folding is a problem of large interest since it concerns the mechanism by which the genetic information is translated into proteins with well defined three-dimensional (3D) structures and functions. Recently theoretical models have been developed to predict the protein folding rate considering the relationships of the process with tolopological parameters derived from the native (atomic-solved) protein structures. Previous works classified proteins in two different groups exhibiting either a single-exponential or a multi-exponential folding kinetics. It is well known that these two classes of proteins are related to different protein structural features. The increasing number of available experimental kinetic data allows the application to the problem of a machine learning approach, in order to predict the kinetic order of the folding process starting from the experimental data so far collected. This information can be used to improve the prediction of the folding rate. In this work first we describe a support vector machine-based method (SVM-KO) to predict for a given protein the kinetic order of the folding process. Using this method we can classify correctly 78% of the folding mechanisms over a set of 63 experimental data. Secondly we focus on the prediction of the logarithm of the folding rate. This value can be obtained as a linear regression task with a SVM-based method. In this paper we show that linear correlation of the predicted with experimental data can improve when the regression task is computed over two different sets, instead of one, each of them composed by the proteins with a correctly predicted two state or multistate kinetic order.Comment: The paper will be published on WSEAS Transaction on Biology and Biomedicin

A Dynamical Study of the Non-Star Forming Translucent Molecular Cloud MBM16: Evidence for Shear Driven Turbulence in the Interstellar Medium

We present the results of a velocity correlation study of the high latitude cloud MBM16 using a fully sampled $^{12}$CO map, supplemented by new $^{13}$CO data. We find a correlation length of 0.4 pc. This is similar in size to the formaldehyde clumps described in our previous study. We associate this correlated motion with coherent structures within the turbulent flow. Such structures are generated by free shear flows. Their presence in this non-star forming cloud indicates that kinetic energy is being supplied to the internal turbulence by an external shear flow. Such large scale driving over long times is a possible solution to the dissipation problem for molecular cloud turbulence.Comment: Uses AAS aasms4.sty macros. Accepted for publication in Ap

Relationship between mechanical-property and energy-absorption trends for composite tubes

U.S. Army helicopters are designed to dissipate prescribed levels of crash impact kinetic energy without compromising the integrity of the fuselage. Because of the complexity of the energy-absorption process it is imperative for designers of energy-absorbing structures to develop an in-depth understanding of how and why composite structures absorb energy. A description of the crushing modes and mechanisms of energy absorption for composite tubes and beams is presented. Three primary crushing modes of composite structures including transverse shearing, lamina bending, and local buckling are described. The experimental data presented show that fiber and matrix mechanical properties and laminate stiffness and strength mechanical properties cannot reliably predict the energy-absorption response of composite tubes

Kinetic and dynamic data structures for convex hulls and upper envelopes

AbstractLet S be a set of n moving points in the plane. We present a kinetic and dynamic (randomized) data structure for maintaining the convex hull of S. The structure uses O(n) space, and processes an expected number of O(n2βs+2(n)logn) critical events, each in O(log2n) expected time, including O(n) insertions, deletions, and changes in the flight plans of the points. Here s is the maximum number of times where any specific triple of points can become collinear, βs(q)=λs(q)/q, and λs(q) is the maximum length of Davenport–Schinzel sequences of order s on n symbols. Compared with the previous solution of Basch, Guibas and Hershberger [J. Basch, L.J. Guibas, J. Hershberger, Data structures for mobile data, J. Algorithms 31 (1999) 1–28], our structure uses simpler certificates, uses roughly the same resources, and is also dynamic