Mega Max Free Download Size

[Melanie Wendelberger]

Of course down at the bottom of the programming window it will show how much room you have used of the total room available, but the test program was just below this maximum. I could get it to compile and everything, and it would try to upload to the Arduino, but after a minute or so of uploading it would error out.

I know what you're thinking (maybe): What was the error code? Sorry, I didn't think to copy it at the time, as this likely won't actually be critical to "real" programs that I make, but I am curious to know if any of you know off hand what is going on and what the actual program size limit is.

Upon further testing using the modified Blink sketch (as described above), I was able to get a maximum total of 24434 lines and 245750 bytes to upload and run on my Arduino without error, and was able to get a maximum total of 24436 lines and 245770 bytes to upload and run, but with the following error:

Just because you can load 25k lines of blink sketch and get this very simple program to run on the Mega, does not mean (in fact it is unlikely) that a more complex program of 25k lines, including the usual complement of variables for a program of this size, will not run out of SRAM.

It's kind of interesting to observe that the LED still noticeably flashes on and off at the millisecond time frame. (It helps to wave the Arduino quickly back and forth to see the individual flashes.)

Thanks for the additional info, Lemming. It's appreciated. I was thinking that a more normal program with a wider variety of components would likely have additional limitations, but wasn't sure. Still, I was curious to find out what the effective maximum program size is, and why I couldn't upload up to the amount that is listed as the maximum.

As you may have guessed, I'm new to the Arduino, and don't have an extensive programming background. I'm having fun playing around with it and testing it out though, and very much appreciate all the on-line resources here and the feedback from other members.

I think there are various problems with the Mega, which get less attention because it is much less popular. Some of the bootloaders have issues. I can't say more from memory, but I would Google "Mega2560 bootloader" and see what you come up with.

I ran into this problem recently and really need that extra 3% so delved into the problem. I'm not experienced with all of the behind the scenes goings-on, but did manage to find the problem and fix it!

The issue is the last block (8K) isn't being erased prior to programming (by the boot) due to an error in the STK500boot.c
So a fresh chip with the boot installed will program to nearly 100% only once. Following modifications to the sketch will bring up an error as the un-erased section is AND'ed with the new data thus not matching the verification file. Usually the mismatch is at 0x3C000 but sometimes 0x3C001 or 2 or 4 as the by coincidence the data at those addresses may be the same.

As I mentioned above I don't know much about the "hidden" arduino stuff and how to create a new HEX file from all this. I used "make" in command prompt to try but it only created a atmega1260 hex, so I commented out mega1260 from the stk500boot.c and the Makefile as mega2560 follows it, and voila! a new stk500boot_v2_mega2560.hex was made.

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Protein sequences vary by more than ten orders of magnitude in thermodynamic folding stability2 (the ratio of unfolded to folded molecules at equilibrium). Even single point mutations that alter stability can have profound effects on health and disease3,4, pharmaceutical development8,9,10 and protein evolution5,6,7. Thousands of point mutants have been individually studied over decades to quantify the determinants of stability11, but these studies highlight a challenge: similar mutations can have widely varying effects in different protein contexts, and these subtleties remain difficult to predict despite substantial effort12,13. In fact, even as deep learning models have achieved transformative accuracy at protein structure prediction1, progress in modelling folding stability has arguably stalled14,15. New high-throughput experiments have the potential to transform our understanding of stability by quantifying the effects of mutations across a vast number of protein contexts, revealing new biophysical insights and empowering modern machine learning methods.

Here we introduce cDNA display proteolysis, a powerful high-throughput stability assay, and use it to produce a large dataset of 776,298 folding stability measurements. This method combines the strengths of cell-free molecular biology and next-generation sequencing and requires no on-site equipment larger than a quantitative PCR (qPCR) instrument. Assaying one library (up to 900,000 sequences in our experiments) requires one week and reagents costing about US$2,000, excluding the cost of DNA synthesis and sequencing. Compared with mass spectrometry-based high-throughput stability assays16,17, cDNA display proteolysis achieves a 100-fold larger scale and can easily be applied to study mutational libraries that pose difficulties for proteomics. Compared with the previous yeast display proteolysis method18, cDNA display proteolysis resolves a wider dynamic range of stability and is more reproducible even at a 50-fold larger experimental scale. Large-scale proteolysis data have already had a key role in the development of machine learning methods for protein design and protein biophysics19,20. The cDNA display proteolysis method massively expands this capability and has the potential to expand our knowledge of stability to the scale of all known small domains.

This strong three-way coupling is noteworthy because the interactions do not appear in the deposited NMR ensemble (PDB ID: 2LGW; Extended Data Fig. 8a,b). The NMR ensemble for 2LGW positions Y5 (Y3 in our numbering) away from the helix containing R62 and D66, making the interaction network impossible. However, the AlphaFold-predicted structure shown in Fig. 4g (the highest confidence model out of five predictions) does include these interactions, which are also seen in other J-domain crystal structures from Caenorhabditis elegans (PDB ID: 2OCH) and Plasmodium falciparum (PDB ID: 6RZY). The strong couplings that we identify support the AlphaFold model and suggest the deposited ensemble is missing conserved interactions that form in HSJ1a, perhaps owing to the specific experimental conditions used. This example illustrates how large-scale folding stability measurements can reveal the thermodynamic effects of a critical interaction even when that interaction is not present in the deposited NMR structure. Notably, AlphaFold itself does not always predict this network either, depending on the specific linkers used (Extended Data Fig. 8d,e).

The scale of cDNA display proteolysis makes it straightforward to characterize unique cases such as these, which can serve as stringent tests for models of folding stability. Strong third-order couplings like this example also present a special challenge for computational models that calculate stabilities by summing interaction energies between pairs of residues using a single reference structure. Deep learning models that implicitly represent conformational landscapes31 may be more promising, but training these models using large-scale thermodynamic measurements will be essential to achieve their potential.

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