A framework for identifying affinity classes of inorganic materials binding peptide sequences

With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences.

Modulation of corticomotor excitability after maximal or sustainable-rate repetitive finger movement is impaired in Parkinson’s disease and is reversed by levodopa

Objectives: In healthy subjects, fatiguing exercises induce a period of post-exercise corticomotor depression (PECD) that is absent in Parkinson’s disease (PD). Our objective is to determine the time-course of corticomotor excitability changes following a 10-s repetitive index finger flexion–extension task performed at maximal voluntary rate (MVR) and a slower sustainable rate (MSR) in PD patients OFF and ON levodopa.
Methods: In 11 PD patients and 10 healthy age-matched controls, motor evoked potentials (MEPs) were recorded from the extensor indicis proprius (EIP) and first dorsal interosseous (FDI) muscles of the dominant arm immediately after the two tasks and at 2-min intervals for 10 min.
Results: In the OFF condition the PECD was absent in the two test muscles after both the MVR and MSR tasks. In the ON condition finger movement kinematics improved and a period of PECD comparable to that in controls was present after both tasks.
Conclusion: The absence of PECD in PD subjects off medication indicates a persisting increase in corticomotor excitability after non-fatiguing repetitive finger movement that is reversed by levodopa.