Partial order relation-based gene ontology embedding improves protein function prediction.

Protein annotation has long been a challenging task in computational biology. Gene Ontology (GO) has become one of the most popular frameworks to describe protein functions and their relationships. Prediction of a protein annotation with proper GO terms demands high-quality GO term representation learning, which aims to learn a low-dimensional dense vector representation with accompanying semantic meaning for each functional label, also known as embedding.

Effect of Multiple Annular Plates on Vibration Characteristics of Laminated Submarine-like Structures.

A numerical model for the prediction of vibration behaviors of a laminated submarine structure consisting of spherical, cylindrical, and cone shells with multiple built-in annular plates is reported in this article. With the aid of the first-order shear deformation theory (FSDT) concerning plates and shells, the energy expressions of each substructure are derived. The displacement functions in the energy functionals are expanded by the employment of Legendre orthogonal polynomials and circumferential Fourier series.

Meshless Chebyshev RPIM Solution for Free Vibration of Rotating Cross-Ply Laminated Combined Cylindrical-Conical Shells in Thermal Environment.

This paper provides a numerical solution to the vibration of a rotating cross-ply laminated combined conical-cylindrical shell in the thermal environment. Its numerical discrete solution method uses the meshless method. The combined shell assumed the temperature independence of material property is divided to the fundamental conical and cylindrical shell substructures, and the theoretical formulation for each substructure is derived based on the first order shear deformation theory (FSDT) and Hamilton's principle.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins.

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes.

A three dimensional visualisation approach to protein heavy-atom structure reconstruction.

Background: A commonly recurring problem in structural protein studies, is the determination of all heavy atom positions from the knowledge of the central α-carbon coordinates.

Results: We employ advances in virtual reality to address the problem. The outcome is a 3D visualisation based technique where all the heavy backbone and side chain atoms are treated on equal footing, in terms of the Cα coordinates.

A comparison of reduced coordinate sets for describing protein structure.

In all-atom molecular simulation studies of proteins, each atom in the protein is represented by a point mass and interactions are defined in terms of the atomic positions. In recent years, various simplified approaches have been proposed. These approaches aim to improve computational efficiency and to provide a better physical insight.

Discrete Frenet frame, inflection point solitons, and curve visualization with applications to folded proteins.

We develop a transfer matrix formalism to visualize the framing of discrete piecewise linear curves in three-dimensional space. Our approach is based on the concept of an intrinsically discrete curve. This enables us to more effectively describe curves that in the limit where the length of line segments vanishes approach fractal structures in lieu of continuous curves.

Towards quantitative classification of folded proteins in terms of elementary functions.

A comparative classification scheme provides a good basis for several approaches to understand proteins, including prediction of relations between their structure and biological function. But it remains a challenge to combine a classification scheme that describes a protein starting from its well-organized secondary structures and often involves direct human involvement, with an atomary-level physics-based approach where a protein is fundamentally nothing more than an ensemble of mutually interacting carbon, hydrogen, oxygen, and nitrogen atoms. In order to bridge these two complementary approaches to proteins, conceptually novel tools need to be introduced.

Discrete nonlinear Schrödinger equation and polygonal solitons with applications to collapsed proteins.

We introduce a novel generalization of the discrete nonlinear Schrödinger equation. It supports solitons that we utilize to model chiral polymers in the collapsed phase and, in particular, proteins in their native state. As an example we consider the villin headpiece HP35, an archetypal protein for testing both experimental and theoretical approaches to protein folding.

Topological solitons and folded proteins.

We argue that protein loops can be described by topological domain-wall solitons that interpolate between ground states which are the α helices and β strands. We present an energy function that realizes loops as soliton solutions to its equation of motion, and apply these solitons to model a number of biologically active proteins including 1VII, 2RB8, and 3EBX (Protein Data Bank codes). In all the examples that we have considered we are able to numerically construct soliton solutions that reproduce secondary structural motifs such as α-helix-loop-α-helix and β-sheet-loop-β-sheet with an overall root-mean-square-distance accuracy of around 1.