Nanotechnology Tools - Essay Example

Introduction Atomic force microscopy, the optical tweezers, alongside other na chnology tools has made it possible to induce and monitor large conformational changes in biomolecules (Salam 45). These studies are often been performed in helping asses the biomolecules structure, their elastic properties, as well as their ability to work as nanomachines in cells. Stretching studies on protein have increasingly become of particular interest and they have been done in systems more than a hundred. All-atom or coarse-grained simulation [], such as those reported in refs, has helped the interpretation of such experiments possible. However, they have been limited by order 100 ns time scales. They, thus, need the use of large constant pulling speeds, which are quite unrealistic and elucidate the nature of a force clamp (region that is responsible for the force of pulling, which is the largest) Fmax. It is worthwhile noting that virtually all the all-atom, and experimental simulational studies merely address a small fraction of the proteins that are often stored within the Protein Data Bank (PDB). It is, thus, worth considering a large set of proteins in order to determine their largest force of resistance to pulling in any model that allows fast and accurate calculations. In this task, the structure-based model of proteins pioneered by collaborators of Go and applicable is implemented in many projects, seem to be most suitable. This is because the proteins are well defined in respect to the native structure. There are various ways of constructing a structure-based model of proteins. However, their variances differ in the choice of their effective potential, the nature of their local backbone stiffness, course-grained degrees of freedom, and the energy related parameters. The crucial choice concerns making a decision about the interaction between the Count of amino acids as native contacts. Research has shown that organism often try to adapt their proteins in order to function more effectively within their range of environmental temperature. This implies that proteins, in general, have a certain limited temperature range in which the structural range is maintained. Anything that lies outside this specific thermal span causes denaturalization to occur with the corresponding function loss, such as the enzyme activity. Changing the intrinsic thermal stability of proteins can be achieved through alteration of the amino acids or otherwise extrinsically through addition of the suitable stabilizing effectors such as coenzymes, peptides, cations, and membranes. Through this experiment it is clear that proteins can be unfolded by force, temperature and chemical denaturant. The focus of our study on finding the relationships between the critical values of the parameters needed for unfolding in the three scenarios. In order to appropriately make comparison between the mechanical strength of proteins and their thermal stability, there was need to correlate Cm, Tm, and normalized B-factor and F-max. In this regard, a mode of experiment B-factors from the PDB database, the experimental lists of protein resilience, single molecule pulling, and protein motion by ANM were used in this study. Materials and Methods The design method to be applied in this study would be a quantitative research design. In social sciences, quantitative research is used to refer to systematic investigation of a phenomenon through a computation technique. The aim of the quantitative research is to employ and come up with mathematical theories or models that pertain to the phenomena. Measurement processes are key elements as far as quantitative research is concerned since they provide fundamental connections linking mathematical expressions and empirical observations. A quantitative research design determines the relationship between two things (dependent and independent variable). This design can either be experimental or descriptive. In experimental design measurements of the participants is made before and after the given treatment. Descriptive design, on the other hand, involves one measurement. In this study, experimental research design would be the most appropriate design to be used in the collection of data. This is a design that utilized a number of variables within one specific study. This design is appropriate because it normally gives out unbiased outcome and has no peculiar methodology because it gives out a lot of flexibility in the research. This design is normally an aggregate of analysis of data. This design involves establishing quantifying variable relationships, and it is employed in gathering data that are empirical on social science discipline. Additionally, with the use of a quantitative research method, an in-depth analysis can easily be archived that would satisfy the objectives of the entire study. Data collection in quantitative research design involves observing and measuring variables on the given subject like in the case of this study the subjects under research are thermal properties of proteins and protein mechanical properties. In this case, the correlation statistics of excel that was used was the Pearson function. The example of excel used in this experiment is shown below Experimental Table 2 PBD Mean of normalized B-factor Fmax [pN] 1emb 1.43E-16 350 1wit 3.01E-10 230 1ubq 1.50E-09 220 1tit -6.08E-09 210 1hz6 2.41E-09 152 1ten -1.50E-09 135 1g1c 2.23E-09 127 1vsc -2.504E-09 95 1cfc 5.04E-09 80 1rsy 7.02E-10 80 1bni 1.57E-09 70 1aj3 1.06E-09 54 2rn2 1.74E-09 20 Correlation -0.3087734 Theoretical Table 2 PBD Fmax [pN] 1emb 1.43E-16 110 1wit 3.01E-10 300.3 1ubq 1.50E-09 240.9 1tit -6.08E-09 231 1hz6 2.41E-09 358.6 1ten -1.50E-09 272.8 1g1c 2.23E-09 470.8 1vsc -2.504E-09 81.4 1cfc 5.04E-09 200.2 1rsy 7.02E-10 238.7 1bni 1.57E-09 152.9 1aj3 1.06E-09 200.2 2rn2 1.74E-09 121 1yn4 215.6 1c78 359.7 2sak 398.2 1v5o 463.1 1sp0 346.5 1so9 336.6 1sn0 386.1 1oo2 330 1i3v 342.1 1i9e 349.8 1nam 330 1eaj 379.5 1kiq 348.7 1f5w 324.5 2ncm 345.4 1pgx 376.2 1m94 330 1anu 331.1 1eta 295.9 1kip 311.3 1sn2 339.9 1tum 343.2 1nme 315.7 1h5b 323.4 1npu 361.9 1mvf 386.1 43c9 335.5 1bzd 338.8 1a2y 319 1km7 282.7 1lve 297 1b88 338.8 1eo6 365.2 1ie4 291.5 1k53 302.5 1kgi 312.4 1oau 282.7 1vhp 325.6 1h8c 319 1jf8 308 1jrk 334.4 1sn5 322.3 1wtl 331.1 1amx 301.4 1qd0 332.2 1ufy 303.6 1mg4 299.2 1p7e 280.5 1j05 289.3 1jhl 309.1 1bmz 335.5 1bvk 291.5 1c08 297 1oar 320.1 1ttc 330 1pun 310.2 1pus 394.9 1wiu 339.9 1gko 298.1 1n4x 343.2 1nvi 275 1fvc 280.5 1ugm 295.9 1igd 289.3 1ivl 284.9 1w19 332.2 1kir 306.9 1kmt 298.1 1l2n 293.7   1dfu 299.2 2rox 327.8 1oaq 286 1rlf 322.3 1tvd 350.9 2dlf 301.4 2imm 325.6 1pga 297 1b9r 313.5 1pav 291.5 1i3o 275 1bm7 319 1k26 266.2 1lqb 302.5 1tbe 319 1gb4 284.9 1rbj 279.4 1tfp 338.8 1tyr 316.8 1ves 327.8 1vfb 327.8 1lsl 313.5 1hz5 1py9 327.8 1fmf 283.8 2try 295.9 4lve 330 1gke 301.4 1etb 314.6 1i8k 339.9 1ict 300 1pqe 266.2 1gnu 276.1 1kot 300.3 1ui9 324.5 1w29 289.3 1oax 295.9 1qp1 327.8 1bz8 298.1 1mel 304.7 1f2x 321.2 1em7 311.3 1com 289.3 1lm8 272.8 1dvy 310.2 1f86 269.5 1rnz 303.6 1tjn 389.4 1v80 279.4 1vjk 280.5 1mfw 314.6 1ic4 250.8 2igd 304.7 5lve 286 1ieh 309.1 1dvt 294.8 1ido 292.6 315.7 To achieve this, we used mode of experiment B-factors from the PDB database, the experimental lists of protein resilience, single molecule pulling, and protein motion by ANM. TABLE 2 The listing is from the highest to the lowest experimental value of Fmax (Joanna, 02) Protein References 1v9e carbonic anhydrase II 1100 pN (29) 1n11 Ankyrin*24 450 pN (24,30) 1emb green fluorescent protein (3-132) 350 pN (19,43,44) 1wit I28 titin 230 pN (31) 1ubq ubiquitin N-C 220 pN (4,32) 1nct M5 210 pN (33,34) 1tit titin 210 pN (3) 1qjo E2lip3 N-41 177 pN (21) 1hz6/2ptl protein L 152 pN (35) 1ten fnIII3 135 pN (36) 1emb (3-212) 130 pN (19,43,44) 1g1c M5 titin 127 pN (31) 1fnh fnIII12 124 pN (37) 1emb (132-212) 120 pN (19,43) 1emb (N-C) 104 pN (19,43) 1vsc Mel-CAM 95 pN (28) 1fnh fnIII13 89 pN (37) 1ubq ubiquitin 48-C 76 pN (4,32) 1cfc poly-calmodulin 80 pN (38) 1fnf/1ttf/1ttg fnIII10 78 pN (37,39) 1bni/1bnr-barnase 70 pN (40) 1b6i T4 lysozyme 60 pN (41) 1rsy/1dqv calcium binding 80 pN (38) 1aj3 spectrin R16 54 pN (42) 1ksr/1whl DdFLN 45 pN (19,43,44) 1u4q spectrin r13-r18 35 pN (1,45,46) 1hci spectrin α-actin 38 pN (1,46) 1n11 ankyrin, one subunit 37 pN (24,30) 1rnh/2rn2 ribonuclease H 20 pN (47) (not included) 1qjo E2lpi3 (N-41) 20 pN (21) (not included) Results and Discussion We looked for correlations between Fmax, Tm and Cm from published data. After analyzing the results from the study, the correlation between different variables were calculated as follow. Average of Cm in Urea vs. Experimental Fmax [pN] Correlation is -0.323877109 Average of Cm in Urea vs. Theoretical Fmax [pN] Correlation is -0.5600775 Tm vers. Fmax [pN] Average of Tm in temperature vs. Experimental Fmax[pN] Correlation is0.807328537 Average of Tm in temperature vs. Theoretical Fmax[pN] Correlation is-0.314152322 VS. Tm (in temperature ) Correlation is -0.401340544 VS. Tm (in temperature ) Correlation is -0.471884179 Tm vers. Cm Correlation is -0.034920752 Mean of normalized B-factor vers. Experimental Fmax Correlation is -0.308773411 Mean of normalized B-factor vers. Theoretical Fmax Correlation is 0.203065969 Normalized Maximum B-factor versus. Fmax Correlation is -0.122313048 Apparently, there is strong correlation between the mechanical strength and the thermal stability of proteins. It is worth noting that there are two parameters that are strongly positively correlated. These are Average of Tm in temperature vs. Experimental Fmax[pN] (r = 0.807328537) and the Mean of normalized B-factor against the Theoretical Fmax (r = 0.203065969). The variable that negative correlate include the Average of Cm in Urea and the Experimental Fmax [pN] (r = -0.323877109), the Average of Cm in Ureaand Theoretical Fmax [pN] (r = -0.5600775), Tm versus. Fmax [pN, the Average of Tm in temperature and the Theoretical Fmax[pN] (r = -0.314152322) and the Mean of normalized B-factor and the Experimental Fmax (r= -0.308773411), the Tm and Cm (r = -0.034920752 and Tm, and Cm ( r = -0.034920752) Work Cited Biopys, J. Biophysical Journal. 2008. Retrieved on 31st Oct 2012 from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2134851/table/tbl2/> Joanna I. and Marek Cieplak. Stretching to Understand Proteins—A Survey of the Protein Data Bank. 2008. Web < http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2134851> PDBTM: Protein Data Bank of Transmembrane Proteins. Web Salam, Al Karadaghi. Introduction to protein structure and structural bioinformatics.2012. Web http://www. proteinstructures.com/Structure/Structure/proteinstructure-databases2.html. Read More