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Advances in Mechanical Engineering
Volume 2013 (2013), Article ID 241384, 12 pages
Analysis and Design Optimization of a Compact and Lightweight Joint Torque Sensor for Space Manipulators
1Beijing University of Posts and Telecommunications, Room 803, Student Building/Apartment 8, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
2Beijing University of Posts and Telecommunications, Room 927, KeJiYuan Building, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
Received 22 January 2013; Accepted 28 February 2013
Academic Editor: Seung Bok Choi
Copyright © 2013 Nanzhe Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper describes the development of a compact and lightweight joint torque sensor for space manipulators. Space manipulators with torque sensors can not only apply force control approach to more precise and more dexterous space missions, but also monitor the occurrence of unpredicted events as accidental impacts with objects they have to manipulate. At present, most of the compact torque sensors, designed without consideration of space environment, cannot be directly used in space applications. In this paper, we propose a compact and lightweight design for the joint torque sensor based on strain gauge, for the reason of good physical, chemical, and mechanical stabilities under high temperature environment. In addition, to reduce the interferences by axial forces generated by assembling condition and joint motors, the structural optimization for the sensing element is carried out. The proposed design is simulated by FEM software ANSYS, and it shows successful measurements of the torque with a load capacity of 10 Nm, which is sufficient for the torque generated in robot joints. The effect of axial loading is also analyzed by ANSYS. The designed sensor is manufactured by duralumin alloy. The calibration for the sensor is carried out, and several experiments are conducted to ensure its feasibility with the space manipulator.
1. Introduction
With the rapid development of space technology, the number of the human exploratory activities in space has constantly increased. Besides handling and assembling of large payloads, more precise and more dexterous space missions such as on-orbit maintainability and on-orbit capture appear in space [1–4]. These space missions promote the development of high performance space manipulator systems, which can move as dexterous as the arm of a man [5, 6]. For the successfully accomplishing tasks of these manipulators, it is essential to calculate the torque at each joint of the manipulator. Space manipulators with torque sensors can be not only applied force control approach to more precise and more dexterous space missions such as on-orbit maintainability and on-orbit capture appeared in space, but also monitor the occurrence of unpredicted events as accidental impacts with objects they have to manipulate, which may affect the reliability and safety of the manipulator and lead space missions to failures. The development of torque sensor for robotic system has been of great interest for decades [7–10], as the commercially available torque sensors are expensive, big, and need extra space in joints. In addition, owing to difficulty to customize commercially available sensors, considerable modifications of joints and links in manipulators are usually needed for assembling purpose. Thus, a variety of torque sensors were designed and developed for robotics systems with acceptable performance by different researchers [11–14].
Ktena et al. [11] designed a magnetostrictive sensor made of NiFe thin film electrodeposited on a cylindrical nonmagnetic 316 L stainless steel rod. They succeeded in developing a sensor with low cost and wide sensor range of 150 Nm. However, this sensor is more complex and appropriate for applications, where accuracy of measurement is not very important.
Yang et al. [12] presented a micro six-axis force sensor based on double layer E-type membrane. The size of the proposed sensor is considerably small; however, the moment/force detecting capability is limited to few micro-Newton meters.
Albu-Schäffer et al. [13] proposed compact design torque sensors for implementation of local impedance control of the lightweight robots (LWRs), which are developed at the Institute of Robotics and Mechatronics of the German Aerospace Center. This kind of sensor, based on strain gauge and used a low-pass filtering at 600 Hz, is capable of detecting 100 Nm load with a sensor error of 0.5%. However, on further experimental research, it was found that the axial forces generated by assembling condition and joint motors can easily affect zero drift characteristics, with hysteresis error varying in large range.
Shams et al. [14] developed a compact, lightweight, and monolithic torque sensor for robots with increased range. This sensor uses the optical technique to measure the torque, which is immune to electromagnetic interference and lightweight, and also requires simple electronics. The thickness of sensor is reduced up to 11 mm without reducing the range of the sensor, that is, 40 Nm. However, the sensing element, that is, photo-interrupter, is easily affected by space environment and is not appropriate for space applications, as the output optical power changes greatly along with ambient temperature varying in large range; furthermore, neutron radiation and r-ray irradiation may lead to aging or permanent damage in optical material.
Most of the compact torque sensors, designed without consideration of space environment, cannot directly used in space applications. At the same time, for a good torque sensor, it is essential to withstand varying axial forces exerted by assembling condition or the robot's body. In addition, torque sensors used in robot joints, placed between the output of speed reducer and the link, can increase the flexibility of the robot joint along with stiffness reducing of the sensor, that is, reduce the control precision and robust stability. So, it is understood that the torque sensor for space manipulators must be able to bear the interference from axial forces and have a high radial stiffness with sufficient resolution and accuracy and a good stability in harsh environment for a long time operation. Otherwise, it may lead to abortive space missions due to torque sensor failure.
In this paper, we propose a new compact and lightweight design of joint torque sensor for a load up to 5 Nm. The dimension of the torque is selected by considering the joint size of the space manipulator system (SMS) [15]. Strain gauge technique is used to detect the angular displacement. To reduce the interferences by axial forces, the structural optimization for the sensing element is carried out. As ambient temperature varying in large range in space, strain gauge temperature effects and temperature compensation for the sensor are considered. The designed torque sensor is compact and lightweight. Simulation was done by finite element method (FEM) software ANSYS, and the sensor was manufactured and calibrated. Several experiments (linearity, hysteresis, resolution, and repeatability) were conducted to ensure its feasibility with the space manipulator system.
This paper begins with a brief explanation of the torque sensor components, which follows the detail idea of using strain gauge approach to detect the angular displacement for SMS then discusses the concept design of flexure. Section 3 contains simulations of designed flexure using FEM software. Next section comprises of experiments and results of manufactured sensor. In the end, advantages and outcomes of the designed sensor is discussed, and future work is proposed.
2. The Sensor System
The proposed sensor system can be divided into two main parts. First part consists of the sensing element, that is, strain gauges. The second part is the flexure. Other parts of the sensor are sensor electronics. The strain gauges are appended on the flexure in a way that it can detect the slightest deformation of the flexure. A C program based on a field programmable gate array (FPGA) platform [15] is also developed to sample the data of the torque sensor.
2.1. The Strain Gauges
In recent years, a variety of techniques as optical techniques, magnetic techniques, mechanical oscillator techniques, and surface acoustic wave (SAW) techniques are being developed by different researchers to detect many different physical parameters [11, 14, 16–23], including displacement either linear or angular force and torque. Increased research in the field of torque sensing based on these techniques is due to its compact size, lightweight and simple electronics. However, application of these techniques is limited for intense radiation and large temperature variations (−200°C°C) in space. On the other side, strain gauge technique, the most commonly used technique for torque sensors within the past decades [24–28], is increasingly applied for stress measurement under high temperature environment these years. This makes strain gauge technique a good choice for the torque sensing as we need a compact, lightweight, and reliable joint torque sensor for space applications. Therefore, in this research, the high temperature strain gauge was selected to be used as sensing element in the design torque cell.
The available high temperature strain gauges, whose maximum operating temperature is up to 250°C, are commonly used for structural stress measurement. Gauge selection for space applications mainly needs to meet the following points.(1)Good physical/chemical stability in the whole working temperature range.(2)Good mechanical stability in the whole working temperature range, that is, little gage factor variation with temperature.(3)Strain gauges with self-temperature compensation and high compensating range.(4)High and stable insulation resistance in the whole working temperature range.(5)High fatigue life and low thermal hysteresis.
Typical high temperature foil strain gauges with self-temperature compensation are listed in Table 1. Table 1 shows that almost all of strain-sensitive alloy of high temperature foil strain gauges are constantan (A-alloy) and modified Karma (K-alloy). For best self-temperature compensation effect, A-alloy and K-alloy can alter temperature coefficient of resistance to match the linear expansion coefficient of the designed flexure by changing alloy composition and controlling temperature and time of heat treatment. Typical thermal output variation with temperature for self-temperature-compensated constantan (A-alloy) and modified Karma (K-alloy) strain gages is shown in Figure 1.
As shown in Figure 1, A-alloy strain gages have excellent self-temperature-compensated characteristics in normal temperature. But thermal output of A-alloy strain gages changes greatly with temperature variation −50°C°C, making complicated temperature compensation. On the other side, the thermal output variation of strain gauges made by K-alloy is relatively smooth in the whole working temperature range. This makes K-alloy strain gauges easy to determine temperature compensation point (TCP) and achieve better compensation effects compared with A-alloy strain gauges.
Gauge factors also change with temperature. As shown in Figure 2, gauge factors of A-alloy and K-alloy change with high linearity as the temperature rises, but the gauge factor variation of K-alloy has an opposite tendency with A-alloy. Previous studies have shown that elastic modulus () of the material for designed flexure decreases as the temperature rises [29]. Thus, the sensor output rises under rated load, that is, causing determinate error. And the gage factor increase of A-alloy strain gauges, as the temperature rises, can further increase this error. On the other side, K-alloy strain gauges can just compensate the impact of the decrease of elastic modulus. This makes K-alloy a better choice for high temperature strain measurement. Therefore, in this research the WK-series strain gauges were selected to be used as a sensing element in the design torque cell.
WK-series gages are fully encapsulated K-alloy, equipped with integral high-endurance beryllium copper leadwires. The matrix is a high temperature epoxy-phenolic resin system reinforced with glass fibers. Overall gage thickness is approximately 0.071 mm. WK-series gages have the widest temperature range and the most extensive environmental capability of any general purpose strain gage of the self-temperature-compensated type. Elevated temperature drift of these gages is very low to , and the strain limits for WK gages are approximately %. The fatigue life and operating temperatures for WK-series gages are shown in Figure 3.
2.2. Flexure Design
The designed flexure, installed between the output of harmonic drive gears and the joint link, is the main component of the torque cell as the strain gages are mounted on it to detect deformation. Hence, in the designing of flexure many considerations are done, namely, size, range, compactness, stability over unwanted loads, simplicity for easy manufacturing, and low cost [14]. Among these, three main points are as follows. First, the size of sensor structure should be small and compact enough to be fixed in the space of the robot joint. Secondly, it should be able to withstand the maximum torque generated by the joint motors (5 Nm). Finally, it should have a good stability over unwanted loads, that is, reducing the interferences from axial forces.
Designing flexure is always a difficult task for the researchers, as in flexures design it is required that flexure should be elastic to detect the smallest torsion or deformation caused by the applied torque/force. And, at the same time, the flexure should be stiff enough to pass the torque without any lag or delay. Stiffness also aids the flexure to withstand the load of the structure and shear and axial forces applied on it [14].
Table 2 shows the torque cell design specifications. The spoke-type sensing structure is adopted in the design, and the designed flexure consists of 4 bending beams or spokes of length 16 mm. These spokes are 1 mm thick and 4 mm wide, and they emerge from the inner circle (23) and are connected to the flange, whose inner diameter (Φ) is 55 mm and outer diameter () is 65 mm.
The elastic elements of designed flexure are the spokes. These spokes are responsible for the deformation or the twist when the torque is applied on the joint link. In addition, any torque generated by the motors is transmitted through the base to the flange via these spokes, causing the spokes to deform. This deformation of spokes creates resistance changes of strain gauges mounted on the spokes, as shown in Figure 4. 8 strain gauges are installed on 4 spokes, respectively, forming two symmetrical Wheatstone bridge circuits. Then, the sensor sensitivity can be calculated from sensor output voltage and applied torque as follows:
The output voltage of the full-bridge circuit is known and can be expressed as: where is resistance of a single strain gauge, is excitation voltage of Wheatstone bridge circuits. and are strain and gage factor of the strain gauge, respectively. Then, sensor sensitivity can be derived as
Equation (3) shows that the sensor sensitivity is determined by the strain of the spokes under rated torque when the strain gauge and excitation voltage of Wheatstone bridge circuits have been selected.
3. Simulation
3.1. Stress and Deformation Analysis
FEM software (ANSYS) is applied to simulate and analyze the stress and deformation of the sensor. The final performance of the designed sensor, to a great extent, depends on material properties. Duralumin alloy is considered for simulation because of its lightweight, good mechanical, physical performance as shown in Table 3. Then, the overload performance is analyzed by ANSYS. The sensor is design to withstand the torque of 5 Nm; thus, 10 Nm is tested for the designed torque sensor. Figure 5(a) shows the tangential deformation of the sensor, and Figure 5(b) shows FEM stress analysis, under torque of 10 Nm. The maximum stress value obtained from analysis is MPa this value is less than the material (duralumin alloy) stress, that is, yield = 245 MPa.
In addition, the effect of axial loading is also analyzed by ANSYS. Besides the designed sensor (sensor #1), another spoke-type sensing structure (sensor #2) is designed to test as well, whose design concept is from [13]. The design specifications of sensor #2 are listed in Table 4. Axial loading of 5 N is applied on threaded holes towards single spoke and threaded holes between two spokes, respectively. Simulation results are shown in Figures 6 and 7, and the simulated data of two sensors is shown in Tables 5 and 6. Thus, sensor #1 is found less deformation (0.0073 mm) than sensor #2 (0.0528 mm) under the same axial loading, that is, having a good stability against axial forces.
3.2. Temperature Effect Analysis
According to the design requirements and mission types, a temperature control system is normally installed inside the joint of space manipulators, which makes the joints working in a certain range of temperature. However, as ambient temperature varying in large range may occur during the space missions, the capacity of wide operating temperature within a short period of time for the designed sensor is needed, which increases reliability of the joint control system at the same time.
As mentioned in Section 2, temperature variations affect both the gauge factor of strain gauges and the elastic modulus of elastic elements of designed flexure, as shown in Figures 2(b) and 8(a). Then, the relative error of the output voltage of the full-bridge circuit is evaluated by (2), which indicates that output voltage and . Parameters and are the gage factor of strain gauges and elastic modulus of the elastic elements of designed flexure, respectively. Simulation results are shown in Figure 8(b). Thus, the temperature effect is found weak of 0°C 50°C, and the relative error under rated load is less than 6% in the whole operating temperature range, which is a benefit for compensation of the determinate error caused by temperature variations.
4. Experiments and Results
As shown in Figure 9, the designed flexure is machined from a single piece of duralumin alloy. In order to eliminate the measurement of sensor error caused by axis misalignment error between the sensor, and the flexspline of harmonic drive gear, 8 strain gauges are installed on 4 spokes, respectively, forming two symmetrical Wheatstone bridge circuits. The bridge circuit is excited by single-regulated 5 V DC voltages. Besides the second-order Butterworth filter designed in the torque sensor processing circuit, a Kalman filter implemented in FPGA has also been adopted to reduce the biggish noise of torque information. The output of the sensor processing circuit is interfaced with the FPGA platform, which is used for data acquisition and processing.
Figure 10 shows the 3D model of the test bench designed for calibration and experimentation of the sensor. The test bench consists of a fixed support, a fixed shaft, the torque sensor, and a base to hold the fixed support. Fixed shaft connects the base of the sensor with the fixed support. The test load is applied to the flange of sensor. Moreover, due to the limitation of experimentation, performances of the sensor under high temperature environment are not considered in the experiments, becoming a part of future work.
For experiments, the sensor was calibrated by applying known torques and measuring the output signals from the sensor. The desired torque was achieved by hanging known weight of mass at a distance m. Torque can be calculated by using (4) as follows:
4.1. Linearity
To test the linearity of the sensor, static torque is applied by hanging load at the flange of sensor. The experiment is conducted more than 50 times to ensure the accuracy and repeatability of the sensor. The experiment data for linearity test with error is shown in Table 7. Then, a fitting line is calculated using (5), which is determined by fitting linear function of data analysis and graphing software OriginPro. Figure 11 shows the scatter diagram of the experiment results with the calculated fitting linear. This straight line is almost connected to each point, indicating good linearity of the designed sensor as follow:
4.2. Hysteresis
Hysteresis test is also carried out for the designed sensor by applying load in ascending and descending manners. The experiment data and error between loading and unloading are shown in Table 8, and the graphs of hysteresis and error are shown in Figures 12(a) and 12(b), respectively.
4.3. Resolution
Experiment was done to determine the resolution of the designed torque sensor, that is, the smallest increment that the sensor can reliably detect. The torque is applied on the sensor with 0.1 Nm increment from 1 Nm to 3 Nm by varying weight of the applied load. Stair-type graph for resolution test is shown in Figure 13. The max output span at any applied torque is about 0.03 V, and the min step size is 0.07 V with an average of 0.086 V, as listed in Table 9. Thus, the designed sensor can reliably detect torque increment of 0.1 Nm.
4.4. Repeatability
Moreover, the designed sensor undergoes repeatability test as well. Table 10 shows part of the experiment data for the test, which is measured more than 50 times for each applied torque value. It was found that the sensor accurately detects the applied torque in the range of ±0.04 V. This shows the sensor's tendency to produce the outputs as much closer to targeted values.
5. Conclusion
In this paper, a compact, lightweight, and monolithic joint torque sensor for space manipulator is developed. The thickness of sensor is reduced up to 10.5 mm, and the designed sensor can successfully detect torque up to 5 Nm. Strain gauge technique is used to detect the angular displacement. As ambient temperature varying in large range in space, strain gauge temperature effects and temperature compensation for the sensor are considered. To reduce the interferences by axial forces, the structural optimization for the sensing element is carried out. Simulation was done by FEM software ANSYS. The result of the simulation shows enough load capacity and low sensitivity over unwanted loads of the sensor. In addition, this sensor proves high repeatability and linearity in the experiments. The designed sensor also shows good stability against axial forces. The least count that the sensor can detect with high precision is 0.1 Nm. Thus, the performances of the designed joint torque sensor match requirements from space missions. Future work is to increase the precision of the joint torque sensor and validate performance and effectiveness of the sensor in actual space environment.
This research has been supported by a Grant from the National High Technology Research and Development Program of China (863 Program) (nos. 2005AA742030 and 2010AA7090304) and the Major State Basic Research Development Program of China (973 Program) (no. 2013CB733005).
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Non Migratory Trout Season
The two main non-migratory trout in Northumberland are brown trout (found in rivers, lakes and reservoirs) and rainbow trout (found only in lakes and reservoirs).
The season for brown trout fishing is from 22 March to 30 September in England and 15 March to 6 October in Scotland but local byelaws there often impose a restricted season from 1 April to 30 September.
Again, fishing on Sundays is not permitted on Scottish rivers (the Tweed and Till). Brown trout are much smaller than sea trout.
For some, though, the sport of catching trout in rivers is much more challenging than salmon. Typically, the average weight of a brown trout is 1lb, but exceptionally weights can reach up to 4lbs.
Typically, the average brown trout will weigh 1/3lb but top weights can reach up to 4lbs. Trout may also be fished in still waters.
Still Water Trout
Brown trout are usually wild, rainbow and brook trout are stocked. An Environment Agency Rod Licence is also required for all lakes.

subset: commoncrawl


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The Sunshine Coast Minor Hockey Association had a rewarding weekend on and off the ice, playing host to a pair of successful tournaments in Sechelt and Gibsons.
In the Atom A tournament final Monday afternoon at the Gibsons and Area Community Centre, the Sunshine Coast Blues took gold with a 5-1 win over Seafair.
The Blues finished first in the five-team round robin with a 4-0 record.
Seafair, the North Vancouver Storm and Whitehorse Mustangs all finished the round robin with identical 2-2 records, but were seeded two through four respectively based on goals for and against. West Vancouver Thunder finished fifth in the round robin with a 0-4 record.
Whitehorse won the bronze medal with a 3-2 win over the Storm on Monday morning.
In the Peewee A tournament final on Monday at the Sunshine Coast Arena in Sechelt, Semiahmoo won gold with a 3-1 win over the Blues.
Whitehorse won the bronze medal with a 2-1 win over the North Shore Avalanche.
The Peewee tournament consisted of six teams in two three-team pools.
Whitehorse won pool A with a record of 1-0-2, the Blues were second at 0-1-2 and the North Vancouver Storm were 0-3.
Semiahmoo won pool B with a 2-0-1 record, the West Vancouver Thunder were second with a 1-0-2 record and the Avalanche were third with a 1-1-1 record.
The playoffs opened with the Blues beating Whitehorse 2-0 on Sunday night and Semiahmoo beating West Van 7-1.
In addition to the bronze medal game on Monday, Whitehorse played in the consolation game, beating the Storm 6-3.
"It was a good weekend. Pretty well all the games were very close and competitive and it was nice to see our teams perform so well too," said Brenda Rowe from the SCMHA. "We had a scheduling conflict with West Vancouver as they had to start their playoffs on Monday, so they had to drop out of the tournament. That allowed the North Shore Avalanche to play for the bronze medal, and they had a great game with Whitehorse. Whitehorse was also gracious enough to stick around and play another game against North Vancouver."
Whitehorse brought close to 60 people to the Sunshine Coast for the weekend.
Whitehorse Minor Hockey Association president and coach Carl Burgess said they had a fantastic time and the reception they received from the community was first class.
"We actually dress five to eight travelling teams per year and we have an active managing group that is always looking for tournaments," Burgess said. "Our competitive teams have a challenge where there is not a lot of competition for them, so we are always travelling. The Sechelt area has long been one that our teams have visited and we have met Sechelt at other tournaments, so when this tournament came open, both our Atom and Peewee teams jumped on it.
"The tournament has been fantastic. Not all tournaments end up this good where both our teams have played well. The officiating has been extremely consistent and has been fantastic. The competition has been stiff and the community has been very welcoming to all of us. It's been a great trip and the small town, West Coast hospitality has lived up for sure."
Burgess hopes that other teams from the Sunshine Coast and the Lower Mainland will look at Whitehorse next year as a tournament destination.
"We'd love to return the hospitality," he said. "We have almost 430 players in our association in the community of 26,000 people. We have some fantastic deals through our airline and offer people a fantastic northern experience. We have some great summer hockey games as well, so year-round we have a lot going on. We'd love to see more teams come up and it certainly helps us get some competition too."
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Ke$ha collaborates with Juicy J, Wiz Khalifa and Becky G for the official remix to "Die Young". Produced by Dr. Luke.
PREVIOUS: Ke$ha – Sleazy (rmx) f. Wiz Khalifa, Andre 3000, T.I. & Lil Wayne (Video)
Posted in Singles | Tags: Becky G, juicy j, kesha, wiz khalifa
The funny thing is, this is one of the best verses I've heard Wiz spit in a really long time.
oh dats funny
this gangsta as fuck!!!
dis track is dope as fukk..! But da reson for da song gettin so many nopes, is cause 2db is not the right, "target group" for a kesha song. Even though dis i pretty desent..
of course it is a pop/dance song but both parts were quit dope
this song sends a terrible message to the youth, that's why it got a nope from me. i don't care what kind of techno backdrop or featured rappers it has.
co-sign schuyler fuck this tune and ke 'dollar sign' ha. why would you even want to remix this song. what an awful message it sends out, fuck the live fast die young motto. have you seen the video that shits fucked up. this bitch needs a scrub
damn i thought Juicy was gonna have a more druggy verse and fuck the track up...


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Urban and small stream flood advisory issued - SoCal experiences hailWednesday, August 20th, 2014 Issue 34, Volume 18.
The National Weather Service in San Diego has issued an Urban and Small Stream Flood Advisory for Southwestern Riverside County and Northwestern San Diego County until 6:15 am this morning (Wednesday). Loud thunderstorms and rain has been falling throughout the night. At 3:21 am Doppler radar continued to indicate moderate to heavy rain falling over recent burn scars in areas of Camp Pendleton. Minor flooding is expected at this time however if moderate to heavy rain continues, flash flooding could be possible. At about 4 am and again at 4:45 am heavy rain and hail was experienced in Fallbrook, in addition to thunder and lightning throughout the night. Precautionary and preparedness actions â A Flood Advisory means river or stream flows are elevated or ponding of water in urban or other areas is occurring or is imminent. 5 comments
Putting it mildly, it had to be the loudest thunder I have ever experienced, it knocked me out of bed!
It's good for Fallbrook. Our fruit trees are benefiting from the rain.
That was awesome. I haven't heard thunder and lightning like that in years!! Loved the rain, too!!! It was so close there has to be a lot of trees down. Please post!!
I've been up since it started around 1:30am. And then the hail around 4am. I looked out the back patio and saw a sheet of white. Amazing the weather we have right now.
We received over 1/2" of rain this morning in south Hemet. Crazy thunder/lightning show too!
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ISRN Computational Biology
Volume 2014 (2014), Article ID 581245, 11 pages
Application of Hybrid Functional Groups to Predict ATP Binding Proteins
Center for Bioinformatics & Computational Biology, Department of Biology, Jackson State University, Jackson, MS 39217, USA
Received 2 September 2013; Accepted 29 October 2013; Published 8 January 2014
Academic Editors: S.-A. Marashi and B. Oliva
Copyright © 2014 Andreas N. Mbah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The ATP binding proteins exist as a hybrid of proteins with Walker A motif and universal stress proteins (USPs) having an alternative motif for binding ATP. There is an urgent need to find a reliable and comprehensive hybrid predictor for ATP binding proteins using whole sequence information. In this paper the open source LIBSVM toolbox was used to build a classifier at 10-fold cross-validation. The best hybrid model was the combination of amino acid and dipeptide composition with an accuracy of 84.57% and Mathews Correlation Coefficient (MCC) value of 0.693. This classifier proves to be better than many classical ATP binding protein predictors. The general trend observed is that combinations of descriptors performed better and improved the overall performances of individual descriptors, particularly when combined with amino acid composition. The work developed a comprehensive model for predicting ATP binding proteins irrespective of their functional motifs. This model provides a high probability of success for molecular biologists in predicting and selecting diverse groups of ATP binding proteins irrespective of their functional motifs.
1. Introduction
Recent advances in the next generation sequencing and human genome projects have resulted in rapid increase of protein sequences, thus widening the protein sequence-structure gap [1, 2], leading to diverse protein functions from common family. Computation prediction tools for predicting protein structure and function are highly needed to narrow the widening gap [3]. The ATP binding proteins (ATP-BPs) are a diverse family of proteins in terms of amino acid sequences, function, and their three-dimensional structures. These proteins hydrolyze ATP to provide the energy necessary to drive biochemical reactions in the cell [4]. There are two distinct functional groups of ATP binding proteins.
The first functional group has the Walker A motif [GXXXXGK (T/S) or G-4X-GK (T/S)] in their sequences for ATP binding [5]. Many members are transmembrane proteins and are responsible for transporting a wide variety of substrates across extra- and intracellular membranes [6]. The biochemical functions of ATP binding proteins are well exhibited within the ABC transporters group. In bacteria cell, ABC transporters pump substances such as sugars, vitamins, and metal ions into the cell, while in eukaryotes they transport molecules out of the cell [7]. They are also known to transport lipids and play a protective role to the developing fetus against xenobiotics [7]. ABC transporters are crucial in the development of multidrug resistance, with the ATP binding sites exploitable as targets for chemotherapeutic agents [8]. The mechanism of action in multidrug transportation is unclear. However, one model called hydrophobic vacuum cleaner states that, in P-glycoprotein, the drugs are bound indiscriminately from the lipid phase based on their hydrophobicity [9].
The second evolutionary diverse functional class of ATP binding proteins is called universal stress proteins (USPs). The universal stress proteins (USPs) are found in diverse group of organisms like archaea, eubacteria, yeast, fungi, and plants; their expressions are triggered by variety of environmental stressors [10]. These stressors might include but are not limited to starvation of nutrients such as carbon, nitrogen, phosphate, sulfate and the required amino acid and variety of toxicants and other agents such as heavy metals, oxidants, acids, heat shock, DNA damage, phosphate, uncouplers of the electron transport chain, and ethanol [11, 12]. The USPs bind to ATP through the ATP binding motif [G-2X-G-9X-G(S/T)] [13]. Members of the USPs will segregate into two groups based on whether or not they bind to ATP [13].
Experimental efforts are underway to determine the function of newly discovered proteins [14], but these experimental methods are costly and time consuming and at times are unsuccessful, due to the complexity involved in protein crystallization process. Several methods had been studied based on predicting ATP binding residues from their known structural features but with low accuracies [15, 16]. Some predictors of ATP binding proteins have been developed with promising results such as those in [17, 18], including Green et al. [19] article on an effective method to recognize ATP binding proteins by testing parallel cascade identification and KNN. Unfortunately these methods were adapted to ATP binding proteins containing only the classical Walker A motif [G-4X-GK (T/S)] in their sequences. The objective of this research reported here was to introduce a classifier built from a pool of protein sequences containing both ATP binding motifs of G-4X-GK (T/S) and G-2X-G-9X-G(S/T). To achieve the objective, support vector machine (SVM) approach is proposed which predicts protein functions based on the discriminative features that map protein sequences to biological functions [20–23] using the sequence pool ATP hybrid motifs.
There is aneed to develop an automated predictor for ATP binding USP encoded proteins to speed experimental designs and study how these proteins function under diverse environmental stressors. This research has developed hybrid ATP binding protein predictor using the open source LIBSVM toolbox classification. The best model was the combination of amino acid and dipeptide composition of the sequences with an accuracy of 84.57% and Mathews correlation coefficient (MCC) value of 0.693%. This model shows a striking overall performance in sensitivity (82.46%), specificity (87.00%), and precision (87.85%) with area under the ROC curve (AUC) value of 0.849219. The general trend shows that combinations of descriptors perform better and improved the overall performances of individual descriptors, particularly when combined with amino acid composition. This model provides a high probability of success for molecular biologists in predicting and selecting diverse motif groups of ATP binding proteins.
2. Materials and Method
2.1. Datasets
Balanced datasets of ATP and non-ATP binding proteins were constructed from the UniProt protein database (UniProt release 2011_11) (http://www.uniprot.org/), Protein Data Bank (http://www.rcsb.org/pdb/home/home.do), IMG/M database (http://img.jgi.doe.gov/cgi-bin/m/main.cgi), and published literatures [24–26] which contain diverse universal stress proteins.
2.1.1. Extraction of Walker A Motif Dataset
A total of 2000 protein sequences which belong to Walker A motif positive dataset were retrieved. Redundancy due to homologous sequences was removed using CD-HIT [27] and PISCES [28] servers at a threshold of 25%. This threshold statistically retains adequate number of protein sequences for analysis as well as avoids bias that might result from high homology. Dataset obtained was manually reviewed through literature search and information from the protein data bank [2] to ensure they represent ATP binding proteins. A total of 100 sequences were randomly selected from the original dataset and retained for training and testing to represent Walker A motif positive (ATP binding) dataset. The Walker A motif negative dataset (non-ATP binding) was taken from Yu et al. 2006 [29]. This was the "negative" dataset used for nucleic acid binding proteins. This is because ATP binding proteins are members of nucleotide binding protein family; hence the negative dataset used in [29] for predicting nucleotide binding protein family was considered useful. Redundancy was also maintained at 25% threshold and each protein was verified to be non-ATP binding using both the literature and protein data bank information. A total of 100 sequences were also randomly selected from [29] and retained for training and testing to represent Walker A motif negative (non-ATP binding) dataset.
2.1.2. Extraction of USP Protein Dataset
The extracted USP sequences were tested for the presence or absence of the G-2X-G-9X-G(S/T) motif in their sequences using the NCBI conserved domain search tool [30]. The USP sequences were divided into two groups based on the presence or absence of ATP binding motif [13]. The redundancy was also maintained at 25% threshold and 100 sequences were selected for each class of proteins (200 sequences in total).
The overall summary of the data prepared for analysis was as follows: (i) 100 ATP binding proteins with Walker A motif; (ii) 100 without ATP binding proteins without Walker A motif, (iii) 100 USP sequences with ATP binding motif [G-2X-G-9X-G(S/T)], and (iv) 100 USP sequences without ATP binding motif [G-2X-G-9X-G(S/T)]. The 400 sequences were separated into two hybrid groups as follows: 200 ATP binding sequences and 200 sequences without ATP binding motifs and were used to generate the feature vector. The feature vector was generated from the entire sequences of the proteins (not only the ATP-binding domains) via PROFEAT server using 1497 descriptor set [31]. Physicochemical and sequence attributes of biologically informative were prioritized for investigation. The attributes were incorporated into LIBSVM classifier to find the best hybrid model for predicting ATP binding proteins.
2.2. LIBSVM Classifier
Support vector machines (SVM) recognized objects to be classified as points in a high-dimensional space needing a hyperplane to separate them [32]. The biological molecules are represented with descriptor set. With a proper mapping furnished by a kernel function, SVM classifiers separate transformed data with a hyperplane in a high-dimensional space to predict the correct classification of protein functional classes. SVMs have been widely used in supervised classification problems in bioinformatics, such as [33–36]. The LIBSVM package which is freely downloadable at (http://www.csie.ntu.edu.tw/~cjlin/libsvm) was adopted and used to evaluate the attributes and build the final classifier, using the radial basis function (RBF) as the kernel function [37–39].
A "grid-search" was employed to select the proper values of the parameter of RBF and the penalty parameter () of the soft margin SVM. was set to and γ to . All the combinations of and γ were tested and the pair with the best cross-validation accuracy for each feature set or combination of feature sets was selected. A smaller γ value makes the decision boundary smoother. The SVM training parameter is the regularization factor, which controls the tradeoff between low training error and large margin [37, 40]. Throughout this work, the parameter was maintained at after trial and error assessment as the best value. The optimal value of γ was obtained for each descriptor set for best results. The entire sets of attributes were evaluated in terms of their association with ATP binding protein and a final subset with good predictive power was selected. In this research a 10-fold cross validation (10CV) was implemented. The objective of training is to maximize the ability of the SVM predictor to discriminate between classes while avoiding overfitting.
2.3. Tenfold Cross-Validation Analysis
The technique to evaluate any newly developed method has become a major challenge to investigators. The jack-knifing leave-one-out cross-validation (LOOCV) [41–43] is the popular technique for evaluating models. During this procedure one sequence is used for testing and the left over sequences are used for training. This process is repeated many times and each sequence is used once for testing. Even though this method is popular, it is computer intensive with considerable labor time.
In this work, 10-fold cross-validation was used to train and test the dataset with sequences randomly partitioned into ten sets. This cross-validation ensures that the dataset was split at the protein level in addition to the stratified partition, thus ensuring a more rigorous evaluation. During the procedure, the positive and negative data samples are distributed randomly into 10 sets or the so-called fold. In each of the 10 round steps, 9 of the 10 sets are used to construct a classifier (training), and then the classifier is evaluated using the remaining set (testing). This procedure was repeated ten times in a manner where each set was used for testing [44, 45]. The overall performance was the average of the performances of all the 10 sets.
2.4. The LIBSVM Performance Evaluation
The standard parameters used in evaluating the performance of the LIBSVM are indicated below. The overall accuracy (Acc) is the intuitive measurement of the performance on a balance dataset whereas Matthew's correlation coefficient (MCC) [46] is more realistic than Acc in measuring performance when using an unbalanced dataset [47, 48]. When both MCC and Acc values are high, the overall performance of the predicted model is better. In addition to Acc and MCC, the following parameters below were also calculated. Sensitivity is the percentage of correctly predicted binding proteins to the total binding proteins. True positive (TP). True negative (TN). False positive (FP) (false alarm). False negative (FN). False positive rate (FPR). Sensitivity/recall or True positive rate (TPR) TPR = TP/P = TP/(TP + FN). Precision = TP/(TP + FP). Accuracy (Acc) = (TP + TN)/(P + N) = (TP + TN)/(TP + TN + FP + FN). Specificity (SPC) SPC = TN/N = TN/(FP + TN) = 1 – FPR. Matthew's correlation coefficient (MCC). ((TP × TN) − (FP × FN))/[sqrt ((TN + FN) × (TN + FP) × (TP + FN) × (TP + FP))] OR
Here TP is the number of true positives (ATP-BPs), TN is the number of true negatives (non ATP-BPs), FP is the number of false positives, and FN is the number of false negatives.
2.5. Area under the ROC Curve (AUC) for LIBSVM
It is a plot between true positive proportion (TP/TP + FN) and false positive proportion (FP/FP + TN). The StatsDirect was used package to plot ROC and calculates the area under the ROC curve directly by an extended trapezoidal rule [49]. The confidence interval was constructed using DeLong's variance estimate [50] embedded in the statistic package.
3. Results and Discussion
The ATP binding proteins are known to play key roles in the biochemical functioning of the cell. In signaling pathways ATP molecules are substrates for protein kinase phosphorylation. It is difficult to identify ATP binding proteins due to lack of experimentally determined protein structures [51–53]. This is because the growth of protein sequences from various genomic projects exceeds the capacity of experimental techniques in determining protein structures and their binding reactions which are time consuming and at times unsuccessful. Therefore there is an urgent need to develop automated expert methods for determining the functional class of proteins such ATP binding proteins from their primary sequence information.
The general assumption here is that every protein that binds to ATP molecule either USPs or those having Walker A motif will have some common features embedded in their sequences. In both the USP (G-2X-G-9X-G(S/T)) and Walker A (G-4X-GK (T/S)) motifs, the G, K, T, and S denote glycine, lysine, threonine, and serine, respectively, and X denotes any amino acid residue. The lysine (K) residue in the Walker A motif is crucial for nucleotide binding [54] in this class of proteins. It interacts with the phosphate groups of the nucleotide and with the magnesium ion, which coordinates the β- and γ-phosphates of the ATP molecule [55, 56].
The universal stress proteins bind to ATP through the ATP binding motif G-2X-G-9X-G(S/T), with the -G(S)/T as essential residues for ATP binding and phosphorylation [13]. Therefore, members of this class of proteins will segregate into two groups, based on whether or not they bind to ATP [13, 57]. Thus, it is important to identify ATP binding USPs and other ATP binding proteins. Several methods have been studied based on predicting ATP interacting residues if the protein structures are known, with some results showing very low accuracies [15, 16, 58, 59]. This work has predicted ATP binding proteins in general with high accuracy irrespective of their structural information using SVM classifier. The training and prediction statistics for each of the descriptor sets used were visualized and discussed below. The visualizations were constructed using Tableau Public Software (http://www.tableausoftware.com/public).
The objective in this report was to find the best descriptor set which can be use to build a predictive model for a reliable and effective server for predicting ATP-BPs in general, irrespective of their subfunctional classes. Throughout this work, the parameter was maintained at , while the optimal value of γ for each descriptor was obtained and used in evaluating their performances. Their performances were evaluated based on five computed parameters consisting of their accuracies, sensitivities, specificities, precisions, and MCC, after a 10-fold cross validation (CV10).
The performance of pseudo amino acid composition was evaluated with only accuracy due to lack of sufficient sequence information. The lengths of the color coded descriptors were used as a measure of their performances. In terms of accuracy the best descriptor was the combination of amino acid with dipeptide composition (84.57%), followed by amino acid composition alone (83.64%), dipeptide composition (83.17%), and Norm M-B autocorrelation in that order (Figure 1). The pseudo amino acids and Quasi sequence order descriptors performed poorly compared to the other descriptors. However, the overall performances of the other descriptors were better as most of them registered accuracy values greater than 70.00%. These high performers might be due to the rigorous refinement of protein sequences. Thus protein function classification with SVM classifiers can be improved drastically using rigorously refined protein sequences.
The individual performances of amino acid composition (83.64%) and dipeptide composition (83.17%) were increased to 84.57% when both descriptors were combined together. This indicates that the combination of descriptors can enhance the individual performance of other descriptors, particularly those combining with amino acid composition. This is a binary classification problem involving a balance dataset and accuracy (Acc) is the best parameter for evaluating performance based on balance dataset whereas Matthew's correlation coefficient (MCC) is more realistic than Acc when using an unbalanced dataset [47, 48]. But when both MCC and Acc values are high, the overall performance of the predicted model is better.
The performances of the models were evaluated based on MCC (Figure 2). The pyramidal view and the length of the color coded descriptors were used for performance visualization. The best performer was amino acid and dipeptide composition in combination (0.6931) followed by amino acid composition (0.6765), dipeptide composition (0.6637), and Norm M-B autocorrelation (0.6449) in that order. This order is in line with their performances measured using accuracy as the parameter. This result justifies the performance of the overall model. In general the combination of descriptor sets performs better than individual descriptors, particularly when combined with amino acid composition.
Therefore from the statistical point of view the use of combination sets particularly with amino acid composition tend to give better prediction performance than individual-sets [53]. The amino acid composition generally increases the overall accuracies of other descriptors in combination. One of the shortcoming of amino acid composition as a descriptor is that the same amino acid composition may correspond to diverse sequences due to the loss of sequence order [28, 60]. This sequence order information can be partially covered by combination with dipeptide composition, but dipeptide composition itself lacks information on the fraction of the individual residue in the sequence, as such a combination set is expected to give a better prediction result [27, 61] as shown above due to masking effect.
The models were further investigated based on their sensitivity to predict ATP-BPs and the results displayed in pyramidal view (Figure 3). The most sensitive descriptor was amino acid composition (0.875) followed by dipeptide composition (0.8381), amino acid/dipeptide composition in combination (0.8246), and Norm M-B autocorrelation (0.8224) in that order.
These descriptors were among the best four performers in terms of Acc and MCC. Evaluation based on specificity indicates that amino acid composition (0.87) was more specific followed by using the entire feature set (0.8478), Quasi sequence order descriptors (0.8333), and dipeptide composition (0.8257) in that order (Figure 4). This information highlights the vital role played by amino acid composition in protein function predictions in general. Interestingly the Quasi sequence order descriptors (0.9626) had the highest precision followed by amino acid and dipeptide composition in combination (0.8785), entire feature set (0.8692), and Transition (0.8411) in that order (Figure 5).
The overall model evaluation shows that the amino acids and dipeptide composition was the best model for predicting ATP-BPs from diverse functional classes using whole sequence information. The use of "all the descriptor" set did not generally result in a better model in classification. The "all features" descriptor accuracy was 79.9% against 84.57% for amino acids/dipeptide in combination. This finding is in accordance with [62, 63], on their work on molecular descriptors for predicting compounds of specific properties using "all features" set. The reduction in accuracy might be due to noise generated by the use of many overlapping and redundant descriptors. Hence the accuracy of the classifier algorithms can be severely degraded by the presence of noisy or irrelevant features, or if the feature scales are not consistent with their importance in solving the classification problem in question. The performance of the SVM model using ROC plot (Figure 6) has a value of AUC of 0.849219. This highlights a better model based on whole sequence analysis.
4. Conclusions
The prediction of ATP-binding proteins has been exploited using a battery of descriptor sets and a hybrid functional group. Also for the first time the prediction of ATP binding in universal stress proteins had been investigated using the support vector machine. The best hybrid model was the combination of amino acid and dipeptide composition of the sequences with an accuracy of 84.57% and Mathews correlation coefficient (MCC) value of 0.693. The general trend is that combination of descriptors will perform better and improve the overall performances of individual descriptors, particularly when combined with amino acid composition. This model provides a high probability of success for molecular biologists in predicting and selecting diverse groups of ATP binding proteins.
Conflict of Interests
The author reports no conflict of interests in this work including the mentioned trademarks.
The research reported was supported by the National Institutes of Health (NIH-NIGMS-1T36GM095335) and the National Science Foundation (EPS-0903787; EPS-1006883). The content is solely the responsibility of the author and does not necessarily represent the official views of the funding agencies.
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Welcome to the Purdue University Calumet College of Business, the premier affordable college of business in Northwest Indiana and the greater Chicago area. Explore our website to find a wealth of information on our programs that are key to your future success in the business world.
Our College is part of the world-renowned Purdue University system and your experiences in all of our programs will prepare you as a professional ready to meet the needs of today's highly competitive global business environment while being aware of your responsibilities as a concerned citizen.
Several factors contribute to the strength of our College. Our outstanding faculty members are focused on student success and represent a diverse blend of scholars and distinguished business professionals who bring real-world experience to the classroom. Our location in in the Northwest Indiana/Chicago corridor serves as a dynamic learning laboratory for the College. And the curriculum in each of our programs is continuously updated to reflect recent trends in the global business world.
It is my pleasure and privilege to be Dean of the College of Business at Purdue University Calumet at a time when the University is focusing its resources on excellence and student success and the College of Business is transforming itself into a more vibrant, more challenging and more exciting place to learn.
Purdue University Calumet has received specialized accreditation for its business programs through the International Assembly for Collegiate Business Education(IACBE), located in Olathe, Kansas.
The business programs in the following degrees are accredited by IACBE:
- Bachelor of Arts in Business
- Bachelor of Science in Accountancy
- Bachelor of Science in Management
- Bachelor of Science in Computer Information Systems
- Master of Business Administration
- Master of Accountancy
The programs in the White Lodging School of Hospitality and Tourism Management are accredited by Accreditation Commission for Programs in Hospitality Administration (ACPHA).
- Bachelor of Science, Hospitality & Tourism Management
Purdue University Calumet is accredited by North Central Association (NCA).
Jane Mutchler, Ph.D.