milRNApredictor: Genome-free prediction of fungi milRNAs by incorporating k-mer scheme and distance-dependent pair potential.

MicroRNA-like small RNAs (milRNAs) with length of 21-22 nucleotides are a type of small non-coding RNAs that are firstly found in Neurospora crassa in 2010. Identifying milRNAs of species without genomic information is a difficult problem. Here, knowledge-based energy features are developed to identify milRNAs by tactfully incorporating k-mer scheme and distance-dependent pair potential.

ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment.

Background: The knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials.

Protein structure prediction.

Predicting 3D structure of protein from its amino acid sequence is one of the most important unsolved problems in biophysics and computational biology. This paper attempts to give a comprehensive introduction of the most recent effort and progress on protein structure prediction. Following the general flowchart of structure prediction, related concepts and methods are presented and discussed.

Diverse effects of distance cutoff and residue interval on the performance of distance-dependent atom-pair potential in protein structure prediction.

Background: As one of the most successful knowledge-based energy functions, the distance-dependent atom-pair potential is widely used in all aspects of protein structure prediction, including conformational search, model refinement, and model assessment. During the last two decades, great efforts have been made to improve the reference state of the potential, while other factors that also strongly affect the performance of the potential have been relatively less investigated.

Results: Based on different distance cutoffs (from 5 to 22 Å) and residue intervals (from 0 to 15) as well as six different reference states, we constructed a series of distance-dependent atom-pair potentials and tested them on several groups of structural decoy sets collected from diverse sources.

Dynamics and robustness of the cardiac progenitor cell induced pluripotent stem cell network during cell phenotypes transition.

Robustness is a fundamental characteristic of biological systems since all living systems need to adapt to internal or external perturbations, unpredictable environments, stochastic events and unreliable components, and so on. A long-term challenge in systems biology is to reveal the origin of robustness underlying molecular regulator network. In this study, a simple Boolean model is used to investigate the global dynamic properties and robustness of cardiac progenitor cell (CPC) induced pluripotent stem cell network that governs reprogramming and directed differentiation process.

Impact of bounded noise on the formation and instability of spiral wave in a 2D Lattice of neurons.

Spiral waves in the neocortex may provide a spatial framework to organize cortical oscillations, thus help signal communication. However, noise influences spiral wave. Many previous theoretical studies about noise mainly focus on unbounded Gaussian noise, which contradicts that a real physical quantity is always bounded.

Impact of Bounded Noise and Rewiring on the Formation and Instability of Spiral Waves in a Small-World Network of Hodgkin-Huxley Neurons.

Spiral waves are observed in the chemical, physical and biological systems, and the emergence of spiral waves in cardiac tissue is linked to some diseases such as heart ventricular fibrillation and epilepsy; thus it has importance in theoretical studies and potential medical applications. Noise is inevitable in neuronal systems and can change the electrical activities of neuron in different ways. Many previous theoretical studies about the impacts of noise on spiral waves focus an unbounded Gaussian noise and even colored noise.

Noise Decomposition Principle in a Coherent Feed-Forward Transcriptional Regulatory Loop.

Coherent feed-forward loops exist extensively in realistic biological regulatory systems, and are common signaling motifs. Here, we study the characteristics and the propagation mechanism of the output noise in a coherent feed-forward transcriptional regulatory loop that can be divided into a main road and branch. Using the linear noise approximation, we derive analytical formulae for the total noise of the full loop, the noise of the branch, and the noise of the main road, which are verified by the Gillespie algorithm.

plantMirP: an efficient computational program for the prediction of plant pre-miRNA by incorporating knowledge-based energy features.

MicroRNAs are a predominant type of small non-coding RNAs approximately 21 nucleotides in length that play an essential role at the post-transcriptional level by either RNA degradation, translational repression or both through an RNA-induced silencing complex. Identification of these molecules can aid the dissecting of their regulatory functions. The secondary structures of plant pre-miRNAs are much more complex than those of animal pre-miRNAs.

qiRNApredictor: A Novel Computational Program for the Prediction of qiRNAs in Neurospora crassa.

Recently, a new type of small interfering RNAs (qiRNAs) of typically 20~21 nucleotides was found in Neurospora crassa and rice and has been shown to regulate gene silencing in the DNA damage response. Identification of qiRNAs is fundamental for dissecting regulatory functions and molecular mechanisms. In contrast to other expensive and time-consuming experimental methods, the computational prediction of qiRNAs is a conveniently rapid method for gaining valuable information for a subsequent experimental verification.

3DRobot: automated generation of diverse and well-packed protein structure decoys.

Motivation: Computationally generated non-native protein structure conformations (or decoys) are often used for designing protein folding simulation methods and force fields. However, almost all the decoy sets currently used in literature suffer from uneven root mean square deviation (RMSD) distribution with bias to non-protein like hydrogen-bonding and compactness patterns. Meanwhile, most protein decoy sets are pre-calculated and there is a lack of methods for automated generation of high-quality decoys for any target proteins.

What is the best reference state for designing statistical atomic potentials in protein structure prediction?

Many statistical potentials were developed in last two decades for protein folding and protein structure recognition. The major difference of these potentials is on the selection of reference states to offset sampling bias. However, since these potentials used different databases and parameter cutoffs, it is difficult to judge what the best reference states are by examining the original programs.