Monday, December 5, 2011

How to run MOPAC6 on a modern Linux machine

Ahum, so we were trying to test the differences between AM1 and PM3 in GAMESS and MOPAC2009 and surprisingly we found a not insignificant difference in the total energies. The energies are shifted by a constant factor of roughly ~1.000014.

However, going back to the MOPAC manual ( we see that the implementation of physical constants were changed in 1993. The GAMESS implementation of semi-empirical methods is basically the MOPAC6 Fock-formation code with some GAMESS routines to diagonalize the whole shebang (for at least as far as single-point energies goes - I haven't been looking at derivatives yet).

Most likely this difference is due to conversion factors between kcal/mol (what MOPAC will give you) and hartrees (GAMESS). For instance, this table I randomly found on the Internet, which is "taken from and old book by Karplus and Porter", says that 1 Hartree = 27.2107 eV, while Jimmy Stewart (Mr. MOPAC) says it's more like 27.2113961 eV. I guess the hartree must recently have been increased in energy along with the speed of light and neutrinos ...

Anyways, I wanted to go back and try MOPAC6 and see if the problem was in the code there. Compiling the MOPAC6 source code, however, proved very difficult, since the source syntax in certain places is not compatible with a newer gfortran, and not even my fall-back f77 compiler would eat it. However, Intel's trusty old Ifort compiler was willing to eat MOPAC6 code, albeit with a couple of warnings. 
However, then the second problem arised. The Makefile was seemingly buggy and refused to work out-of-the-box on my Ubuntu 10.10 machine. So I decided to make a Makefile that would actually run. The script can be found at the bottom of this page, and compiles like a charm, if you have Ifort. Ifort is free for non-commercial use, and usually also quite a bit faster than gfortran.

However, a possibly more simple (and possibly more fun) option is to install Wine (a Windows compatible environment for Linux) and use Wine to execute Mopac. Jimmy Stewart has a Windows compatible binary version of MOPAC6 available for download. Now, lemme show you how to install Wine and fire up MOPAC! On Ubuntu this is as simple as (let's say you have a file named methan.mop):

$ sudo apt-get install wine   # This installs WINE

$ wine mopac6.exe methan      # This runs MOPAC6 via wine with methan.mop

And that's it! Either get Ifort (free for non-commercial use) or use Wine to run MOPAC6 via the binary. I honestly found the last method to be hilarious, but I guess I'm not too different from the guy below.

The newest MOPAC and GAMESS are available for free for personal and academic use.

UPDATE: Just a short note on how to run the compiled version of MOPAC6. First, rename mopac.exe to MOPAC. Next, set the mopac directory in mopac.csh. Now open a cshell and use the mopac.csh script to envoke MOPAC6:

$ csh                     # Start a cshell
% source mopac.csh        # A script necessary to run MOPAC6
% mopac methan            # To actually run MOPAC6 with the file methan.mop

$ indicates bash shell, and % indicates a cshell.

MOPAC6 Makefile for Intel Ifort: (put it in the source code directory and type 'make')

Saturday, December 3, 2011

Fixing disabled wireless on Lenovo Z360 running Ubuntu 10.10

Ok, this post isn't about chemistry at all. However, I'm going to defend it under the category of free software. Here is a short introduction to my problem. I have a Lenovo Z360 laptop running Ubuntu 10.10. It came with some Windows distribution (are they even called distributions? I haven't had a windows computer since 2004) which I never used. <sarcasm> Thanks to Lenovo, the laptop was set up with some proprietary driver software for my wireless network interface card (NIC). </sarcasm>

After installing Ubuntu 10.10 (AKA the perfect 10) everything was working fine ... until I one day pressed the "turn off wireless" buttons (Fn+F5) to save a tiny amount of battery power. This apparently bypasses the hardware "disable wireless switch" and switches off wireless via some built-in firmware. And since I wasn't using the proprietary drivers in Windows, there was no way to switch wireless back on. 

The tedious solution to my problem was to replace the solid-state drive I had put in myself for the original drive, and boot up in windows, switch on wireless using the proprietary driver software. 
Long story short, I have up until this day always been carrying the original harddive and a small screwdriver with me, in case I should accidentally turn off wifi, so I could perform the wireless-enabling surgical procedure.

However, today I stubled up a more permanent solution. I turns out, that the signal to turn off the wireless NIC is transmitted via pin #20. So to avoid ever turning off wireless you just have to tape over that particular pin - see the picture below. After five minutes of fiddling with tape, tweezers and scissors I managed cut a piece of tape small enough and put it precisely over the pin. 
And lo and behold - it's now impossible to disable the wireless NIC via switches or accidental key-presses!

Thanks to the guys over at for pointing this out! And thanks to Lenovo for this post possible by making hardware that just works out-of-the-box-until-it-doesn't-and-there's-no-way-to-switch-it-back-on-besides-software-only-written-for-one-particular-operating-system.

Code snippets on blogs

I had a brief discussion with Casper Steinmann about how to present code snippets in blog-posts. We both agreed that nice formatting is really important. We also agreed that our previous attempts to do so were (and still are) crummy.

One of the (easy) ways to set up nice formatting can be found over at CraftyFella:

Just put some stuff in your blogger template and all you have to do is wrap your code examples in HTML <pre>  tags. "Pre tags" denote a pre-defined format, and are often used to wrap around code examples, with the color coding pertaining to that particular code-language and an equispaced font, which makes it possible to use indentations properly.

Most popular programming languages are supported in this method. C, C++ C#, python, and probably many others. Hoever, no support for FORTRAN - quantum chemists beware. If you know of a user-friendly syntax highlighter that does FORTRAN, do leave a comment! Everybody loves FORTRAN.

Here is a simple example of how a dumb python script looks after using CraftyFella's guide to set things up:


def print_string(string):
    print string

hello_world = "Hello world, look at my fancy code formatting!"

for i in range(10):

... and a little bit of C++:

//! Return size of collection
//! \return size of collection
unsigned int size() const {
     return data_vector.size();

Sunday, November 6, 2011

Molecular dynamics and supercomputing, past and present

What is the most powerful single computer you can get today, measured in sheer numbers of double precision floating point operations per second (flops)? Possibly, the answer is a rack server (such as the HP half-width 4U in the picture below) with no less than eight(!) Nvidia Tesla M2090 cards installed. Each card offers a theoretical max of 665 Gflops of double precision computing power -- a total of 5.3 Tflops (not counting any CPUs). Now, go to the Top500 and find the list from 10 years ago ... November 2001. Just one of these servers would bring you a safe 3rd place on the list - very impressive for just ONE single machine.


One might argue, that a 4U rack unit is not a very practical computer - so what's the max you can cram into a a standard sized ATX chassis these days? Well, take four of these GPUs (and a good power supply), and you're up to about 2.7 Tflops, theoretical peak. 10 years ago that would bring well into top10 world wide with a box that you could put under your desk!

The Japanese "K Computer" is currently the fastest cluster out there and recently broke the 10 Pflops barrier (10¹⁶ flops)! Now, he question is, when can I expect to have that kind of performance in a desktop PC, and how about my huge ms protein molecular dynamics simulation?

Speculating on the future is difficult, so I'm just going to "re-blog" this review paper I stumbled upon by Michele Vendruscolo and Christopher M. Dobson, "Protein Dynamics: Moore’s Law in Molecular Biology", Current Biology, 21, R68-R70 (2011), which speculates on the future of MD simulations. According to the authors, MD computing power sees exponential growth, so hopefully, I can one day have a 10 Pflops (I'm willing to settle for less) box under my desk. Here are some of the bold predictions from the paper:

  • In 2010 we saw the first nanosecond scale MD simulation of the Ribosome
  • In 2030 we'll be moving up to millisecond  scale MD simulation of the Ribosome
  • in 2050 we'll be doing all-atom dynamics of entire bacteria on a nanosecond scale.

I can't wait to get older and see an all-atom bacteria simulation!

Saturday, October 29, 2011

Compiling DALTON2011 with 64-bit integers, OpenMPI and MKL

I was asked if I could do a blog post about how to compile DALTON2011 in parallel with 64-bit integers. This is not entirely trivial, unless you know what's going on. One of the key ingredients is an MPI that supports 64-bit integers, which OpenMPI does not do out of the box. I found 64-bit integers required to complete all test cases succesfully. This tutorial will assume that you have a working set of Intel compilers.

1) Get the OpenMPI 1.4.3, which is the latest stable version to support 64-bit integers:

tar xjf openmpi-1.4.3.tar.bz2
cd openmpi-1.4.3/

2) Now you're ready to compile OpenMPI. I keep my compiled programs in ~/programs/ -- you can use whatever directory you want, so change the prefix to wherever you want it. If you have root access /opt/ would be a great place to put OpenMPI. First, export the CXX, CC, FC and F77 environment variables to specify that you want to use the dedicated Intel compilers. Then run the configure script with the -i8 flag, specifying support for 64-bit integers:

export CC=icc; export CXX=icpc; export FC=ifort; export F77=ifort
FFLAGS=-i8 FCFLAGS=-i8 ./configure --prefix=/home/andersx/programs/openmpi-1.4.3/
make install

(both configure and make can take quite a while)

3) Now, you need to export the following variables:

export PATH=/home/andersx/programs/openmpi-1.4.3/bin:$PATH
export LD_LIBRARY_PATH=/home/andersx/programs/openmpi-1.4.3/lib:$LD_LIBRARY_PATH

You can also add these lines to you ~/.bashrc file, but I wouldn't recommend this, since you might want another version of OpenMPI for another program, so it's better to only export the variables you need, when you actually need them.

Check that your compiled OpenMPI is working by typing:

which mpirun

This should return something like (in the folder you specified in the --prefix in step 2):


Further more, check that your OpenMPI picked up the -i8 flag properly (the compiler flag that tells to compile for 64-bit integers):

ompi_info -a | grep 'Fort integer size'

This must return size 8:

         Fort integer size: 8

If not, you did something wrong, and the following steps will not work!

4) Go to and download the latest version. Untar it, and head into the DALTON2011 folder:

tar xjf Dalton2011_release_v0.tbz
cd Dalton2011_release/DALTON/

Start the configure script and answer the following questions with:

This appears to be a -linux architecture. Is this correct? [Y/n] y
Do you want 64-bit integers? [y/N] y
Do you want to install the program in a parallel MPI version? [Y/n] y
Compiler /home/andersx/programs/openmpi-1.4.3/bin/mpif90 found, use this compiler? [Y/n] y
(This must be found in the folder where you just compiled and installed OpenMPI in steps 1-3)
Compiler /home/andersx/programs/openmpi-1.4.3/bin/mpicc found, use this compiler? [Y/n] y
(This must be found in the folder where you just compiled and installed OpenMPI in steps 1-3)
Do you want to replace this with your own directory search list? [y/N] n
(Just install whatever BLAS/LAPACK the script finds .. we're going to use MKL, but we need to specify that manually later on, overriding what the script finds here)

The remaining questions you can answer however you want to.

5) Now we need to find out, where MKL is placed on you system. This may vary from version to version of the Intel Compiler suite. First write:

env | grep MKL

This returns something like:


If it doesn't, you haven't got the Intel Compilers (and MKL) properly installed. Now head into this directory and locate libmkl_intel_ilp64.a:

locate libmkl_intel_ilp64.a

This returns something like:


The above directory we will call MKL_LIB_DIR:

export MKL_LIB_DIR=/opt/intel/composerxe-2011.0.084/mkl/lib/intel64

Last thing in this step is to edit the Makefile.config and put the information we now have into it. Open Makefile.config with your favourite editor and replace the line that starts with "LIBS = " with:

LIBS = -L/usr/lib -lmkl_lapack95_ilp64 -Wl,--start-group $(MKL_LIB_DIR)/libmkl_intel_ilp64.a $(MKL_LIB_DIR)/libmkl_sequential.a $(MKL_LIB_DIR)/libmkl_core.a -Wl,--end-group -lpthread
(the above is all in one line)

This will link to the correct MKL library, which supports 64-bit integers (also why they're called ilp64) and made for the Intel compilers. Now it's a good time to make sure that you still have the environment variables from step 3) exported. So now we're ready to actually compile DALTON2011:


(now it's time to get a coffee, and do something else for a long while ... Also compiling DALTON2011 does not seem to work in parallel so no -j8 flags here!)

6) Compiling ends without errors and the following line:

mv ./dalton_mpi.x /home/andersx/zips/Dalton2011_release/bin/dalton_mpi.x

To test your compilation head into the test/ folder and start testing the parallel (these only take a minute or so .. the full test suite takes much longer).

cd test/
./TEST parallel

Hopefully this returns:



date and time         : Sat Oct 29 10:54:20 CEST 2011
If not, something is wrong. :) Now proceed to the full test suite. I use nohup and just check the output in nohup.out.

nohup ./TEST all &
less nohup.out

Hopefully nohup.out end with (after an hour or so):

All tests completed successfully
Perl-style tests computed successfully

Perl-style tests finished!



date and time         : Sat Oct  29 13:02:34 CEST 2011

This means DALTON2011 works!

You can also head over to the DIRAC10 wiki pages, which is very much like compiling DALTON:

For support go to:

I did this on a Intel Core i5-760 with Ubuntu 10.04.3 Server and Intel Compilers version 12.0.0 20101006. The entire test suite took twice as long compiling with GNU compilers, so I recommend staying away from these for a "production" version. Further more MKL is one of the fastest and most stable BLAS and LAPACK libraries there is and it comes precompiled for 64-bit integers with the Intel Compilers, so do use MKL and save yourself a headache! Also GNU compilers >4.4 have compiler issues from what I've heard.

Wednesday, October 26, 2011

Fast Lennard-Jones and electrostatics on a CPU

I recently got interested in programming molecular mechanics force fields on CPUs and GPUs. One of the hurdles you have to get through, is the non bonded interactions, which you have to iterate over all pairs of atoms. It thus scales as N^2 with the number of atoms, whereas the bonded terms scale only linearly. A good example is the OPLS force field (read more on Wikipedia). Here the non-bonded interactions are given by a Lennard-Jones potential term an an electrostatic term:

For more information about molecular modeling and force fields, I can  recommend reading the Molecular Modeling Basics book, which has excellent pages on the subject. You can also just ask in the comment sections, if you have anything. Finally, the code can be found for download at the bottom of this post.

Now let's get started! I've put the coordinates of all atoms in the double coordinates[] 2D array. In the example I'm using the coordinates of the protein structure of Cutinase 1CEX, which has 2867 atoms. I used pdb2pqr for protonation. I'm going to assume (for simplicity), that all interactions use the same parameters. The naive way to implement the non-bonded OPLS interaction could be (in C/C++):
// Lennard-Jones parameters
double A = 10.0;
double C = 10.0;
// Electrostatic parameter
double E = -10.0;

double non_bonded_interaction(const double a[3], const double b[3]) {

     // Fastest way of getting distance
     double distance = sqrt((a[0] - b[0]) * (a[0] - b[0])
                          + (a[1] - b[1]) * (a[1] - b[1])
                          + (a[2] - b[2]) * (a[2] - b[2]));

     return A*pow(distance, -12.0) 
          + C*pow(distance, -6.0)
          + E/distance;

int main() {

     // Set energy to 0
     double energy = 0.0;
     // Loop over all atoms
     for (unsigned int i = 0; i < n_atoms; i++) {

          // Only up to i-1, to avoid double counting 
          // interactions, and self-interactions
          for (unsigned int j = 0; j < i; j++) {

               energy += non_bonded_interaction(coordinates[i],

     std::cout << "E = " << energy << std::endl

     return 0;
Timing this gives 0.0300 µs per iteration with the Intel icpc compiler, and 0.189 µs with the g++ compiler, both with -O3 optimization. 
As I will demonstrate, this is because pow() is horribly implemented in g++. Lets further optimize the non_bonded_interaction() function:
double non_bonded_interaction(const double a[3], const double b[3]) {

     double distance = sqrt((a[0] - b[0]) * (a[0] - b[0])
                          + (a[1] - b[1]) * (a[1] - b[1])
                          + (a[2] - b[2]) * (a[2] - b[2]));

     double distance6 = distance * distance * distance
                      * distance * distance * distance;
     double distance12 = distance6 * distance6;

     return A/distance12 + C/distance6 + E/distance;

Now we get: 0.0295 µs with icpc and 0.0313 µs with g++. Clearly pow() shouldn't be used with GNU compilers!

Next on our list of optimizations is getting rid of divisions. Divisions are performed iteratively by the CPU, while multiplications are done bit-parallel by specialized circuits hard-coded into the CPU silicon. We have three divisions, but we can write everything in terms of only one, using reciprocal distances:

double non_bonded_interaction(const double a[3], const double b[3]) {

     double distance = sqrt((a[0] - b[0]) * (a[0] - b[0])
                          + (a[1] - b[1]) * (a[1] - b[1])
                          + (a[2] - b[2]) * (a[2] - b[2]));

     double inv_distance = 1.0/distance;
     double inv_distance6 = inv_distance * inv_distance
                          * inv_distance * inv_distance
                          * inv_distance * inv_distance;
     double inv_distance12 = inv_distance6 * inv_distance6;

     return A * inv_distance12 
          + C * inv_distance6 
          + E * inv_distance;

This gives us 0.0173 µs with icpc and 0.0189 µs with g++. Still ... there is one division left and also a square-root which is problematic. These are what takes up by far the most time in the calculation. But there is a hacky trick we can use. I'm going to show you how to use an approach by Newton to estimate inverse square roots .. take a look at this Wikipedia page for more info. The method became famous after it was found in the Quake III open source engine. Here it is in double type, so slightly different than the float type example on wikipedia. I'm using two final iterations to achieve sufficient accuracy.

inline double fast_inv_sqrt(double number) {

        long long i;
        double x2, y;
        const double threehalfs = 1.5;
        x2 = number * 0.5;
        y  = number;
        i  = * ( long long * ) &y;
        i  = 0x5fe6ec85e7de30da - ( i >> 1 );
        y  = * ( double * ) &i;
        y  = y * ( threehalfs - ( x2 * y * y ) );
        y  = y * ( threehalfs - ( x2 * y * y ) );
        return y;
Using this, we can avoid ever dividing and taking square roots, and reduce our code to:

double non_bonded_interaction(const double a[3], const double b[3]) {
     double dx = (a[0] - b[0]);
     double dy = (a[1] - b[1]);
     double dz = (a[2] - b[2]);
     double distance_sqr = dx*dx + dy*dy + dz*dz;

     double inv_distance   = fast_inv_sqrt(distance_sqr);
     double inv2_distance  = inv_distance * inv_distance;
     double inv6_distance  = inv2_distance * inv2_distance
                           * inv2_distance;
     double inv12_distance = inv6_distance * inv6_distance; 
     return A * inv_distance12 
          + C * inv_distance6 
          + E * inv_distance;


Now, timings are down to 0.0118 µs for icpc and 0.0155 µs for g++ per iteration. It's hard to optimize the code further from here, using code that isn't very hacky, but we still got a x2.5 times speed-up with the Intel compiler and x12.2! times speed-up with the GNU C++ compiler. Still, we can always try and parallelize the code using OpenMP. This is the easiest way to parallelize code there ever was. We only need to add ONE compiler flag, which tells it to parallelize the next loop. Take a look:

// Loop over all atoms
     for (unsigned int i = 0; i < n_atoms; i++) {

// Tell compiler, to run next in loop in parallel
#pragma omp parallel for reduction(+:energy)

          //Only up to i-1, to avoid double counting interactions, and self-interactions
          for (unsigned int j = 0; j < i; j++) {

               energy += non_bonded_interaction(coordinates[i], coordinates[j]);


To compile we also must remember to link to OpenMP:

icpc -O3 cpuff.cpp -o cpuff -openmp (for Intel)
g++ -O3 cpuff.cpp -o cpuff -fopenmp (for GNU)

On four cores instead of one, this gives us: 0.0042 µs for icpc and 0.0042 for g++ as well. That's a x2.8 and x3.6 speed up for Intel and GNU, respectively! Not too shabby, by no means! Compared to the unparallelized code, this is a x7.1 speed-up for Intel and a whooping x45.0 speed-up for the initial lazy GNU compiled code.

I hope this post will inspire you to play around with these easy-to-do optimizations yourself. You can download code examples here:

cpuff.cpp (the implementation)
coordinates.h (the protein coordinates. I put this in a 2D array to avoid writing a parser)

DISCLAIMER: The above was carried out on an Intel Core i5-760 running 64-bit Ubuntu Server 10.04.3, icpc (ICC) 12.0.0 20101006 and g++ (Ubuntu 4.4.3-4ubuntu5) 4.4.3. Timings are averages over 10 consecutive runs. I compiled everything with -O3 optimization.

Friday, October 21, 2011

C++ timing with millisecond accuracy

How do you know if you have efficiently implemented a routine. A simple routine from Boost can tell you. More specifically the boost::timer function.
Boost is a set of free software libraries that extend the functionality of C++, in the Wikipedia discription. There are tons of very useful features you'd expect to be native to an advanced programming language as C++ but are not.

How to get boost? Usually on Linux you can just say something like apt-get install libboost or yum install libboost. Let me know if you want a tutorial to compile or install boost. The examples below compile with both Intel and Gnu C++ compilers and even Nvidia compilers (I tried NVCC 4.0).

Here is a code example of how to time a routine with boost:

#include <boost/timer.hpp>      // For boost::timer class
#include <iostream>             // For printing

// Some constants
unsigned int x = 20000;
bool flag = true;
unsigned int k = 0;

int main() {
        // Start boost timer.
        boost::timer t;

        // Do a double loop that takes some time.
        for (unsigned int i = 0; i < x; i++) {
                for (unsigned int j = 0; j < x; j++) {
                        k = i + j;
                        if (k % 2 == 0) {
                                flag = false;
                        } else {
                                flag = true;

        // Store elapsed time from boost
        double elapsed = t.elapsed();

        // Print elapsed time from the t object.
        std::cout << "boost/timer says:" << std::endl;
        std::cout << "Total time    = " << elapsed 
                << " seconds" << std::endl;
        std::cout << "Avg time/iter = " << elapsed / (x * x) 
                << " seconds" << std::endl;

        return 0;

I get the following output on my Core i5-760 box and the icpc compiler:

boost/timer says:
Total time    = 0.35 seconds
Avg time/iter = 8.75e-10 seconds

You can use t.reset() if you want to reset the timer in your program.

If you want higher accuraccy, you can use the gettimeofday() function from sys/time.h. This example does not require boost either, but the code is way uglier. This is done very much like in the above code:

#include <sys/time.h>           // For gettimeofday()
#include <iostream>             // For printing

// Some constants
unsigned int x = 20000;
bool flag = true;
unsigned int k = 0;

int main() {
        // Create objects for gettimeofday() timing
        struct timeval start_time;
        struct timeval end_time;

        // Get the time of day -- NULL means we don't care about
        // time zones. Store in start_time.
        gettimeofday(&start_time, NULL);

        // Do a double loop that takes some time.
        for (unsigned int i = 0; i < x; i++) {
                for (unsigned int j = 0; j < x; j++) {
                        k = i + j;
                        if (k % 2 == 0) {
                                flag = false;
                        } else {
                                flag = true;

        //Store time of day in end time
        gettimeofday(&end_time, NULL);

        // Calculate the elapsed time with micro second accuracy from gettime of day
        double start = start_time.tv_sec*1000000 + (start_time.tv_usec);
        double end   = end_time.tv_sec*1000000 + (end_time.tv_usec);
        elapsed     = end - start;

        // Print elapsed time from gettimeofday().
        std::cout << "gettimeofday says:" << std::endl;
        std::cout << "Total time    = " << elapsed 
                << " microseconds" << std::endl;
        std::cout << "Avg time/iter = " << elapsed / (x * x) 
                << " microseconds" << std::endl;

        return 0;

This gives me the following output:
gettimeofday says:
Total time    = 366208 microseconds
Avg time/iter = 0.00091552 microseconds

Happy coding! Let me know if you have questions or comments.

Thursday, October 20, 2011

HOWTO: Mixed basis In Gaussian

One of the many tricks in speeding up quantum chemistry calculations is the use of locally dense basis sets. Maybe it is only necessary to have diffuse functions on certain atoms or you may only need to calculate parts of a system with a high accuracy.
Use of locally dense basis sets is completely legal within the basis set approximation. Just remember, that things like Mulliken charges will often be off, if some atoms have significantly more basis functions than other.
In this example an example, I show how to do a geometry optimization a of methylamine at the Hartree-Fock level of theory with a 6-31G basis set on carbon and nitrogen and a 3-21G basis set on hydrogen atoms. Note that the basis set is specified as "gen". This option tells Gaussian to use the basis set specified at the bottom. A the bottom there is a number for each atom (followed by a 0 for reasons unknown to the author), and the name of the basis set below. Data for each atom is terminated by a line containing four asterisks.

Remember to separate "groups" of data with exactly one blank line and the file must be terminated with exactly two blank lines.

--------------------------Start of file--------------------------
# opt hf/gen geom=connectivity

Title Card Required

0 1
 C                 -7.52265887    1.01208458    0.00000000
 H                 -7.16600444    0.00327457    0.00000000
 H                 -7.16598603    1.51648277   -0.87365150
 H                 -8.59265887    1.01209776    0.00000000
 N                 -7.03265039    1.70504284    1.20025020
 H                 -6.03265041    1.70487221    1.20034129
 H                 -7.36582334    2.64790856    1.20015900

 1 2 1.0 3 1.0 4 1.0 5 1.0
 5 6 1.0 7 1.0

1 0
2 0
3 0
4 0
5 0
6 0
7 0

--------------------------End of file--------------------------

Talk at Novo Nordisk STAR Symposium 2011: "Inferential protein structure determination using chemical shifts"

Slides from the talk:

MC simulation of Protein G (2OED) folding. Crystalk structure in cyan, MC simulation in green:

Energies used were Profasi force field energy and CamShift 1.35 MD energy.
I used PyMol to generate .png files from the MC samples and mencode to merge the .png files into an .avi file.

Monday, October 17, 2011

PDB V3 to V2 file converter and CamShift 1.35 notes

I stubmled upon a program called CamShift 1.35, which predicts chemical shifts from a coordinates in a PDB file. However, everytime I tried to supply it with a seemingly  standard PDB file, adhereing to every PDB naming convention I could find, CamShift 1.35 would complain about missing atoms and used an internal force-field to add hydrogens and whatnot at coordinates that were not correspoding to the coordinates I had supplied.
A little trick I found, was to uncomment the lines 278-279 in the camshift-1.35/bin/camshift.cpp file:

//  for(int r=0;r<rep.size();r++)
//    cout<<"\t"<<p.atom_name(rep[r],Almost::Protein::BASE)<<"\n";

 and the recompile. This will add the names of all atoms that are added. Please note, that protonation states are adjusted entirely by CamShift, so even if you give it a proper PDB file you will have things added, such as H-gamma on cysteine residues, even despite of actual cys-bridges in the supplied PDB file.

At any rate, by doing the above, I discovered that the CamShift 1.35 code uses the old PDB v2 naming convention (which was deprecated during the last millennium). Sadly I couldn't find any file converter that would let convert a standard (currently PDB v3) PDB file to an older v2 type. Not even my all-time favourite file-converter OpenBabel was capable of this. Long story short, I found the proper naming conversions and put it in a short python script.

Usage is like this:

$ python myfile.pdb

This will print the new file to standard out put. If you want it in a new file, simply use the awesomeness of shell:

$ python myfile.pdb > outfile.pdb

Since it's written in standard Python, it will work on any platform (Windows, UNIX, Linux, MacOS, etc) and you need not worry about compiling, just a working Python interpreter is enough.

Have fun converting. Shoot me a message, if you find any bugs or quirks. Download the program here:

(click link or picture to download)

Sunday, August 28, 2011

YouTube Lecture: Novel Enzymes, Rapid Structure Determination, and an Online Computer Game

This is a very interesting video about the general field of computational protein structure prediction - the field in which I am involved. It's a talk by Professor David Baker, Dept. of Biochemistry, University of Washington - probably better known as the guy behind stuff like ROSETTA and FoldIt.


Thursday, August 25, 2011

Installing MOPAC2009 on Ubuntu 10.04 LTS - Lucid Lynx (And keep source codes open!)

If you try and install MOPAC2009 on a standard Ubuntu 10.04 LTS system, you will get a very weird problem. I unzipped MOPAC2009 in /opt/mopac as described in the installation guide. However, when I tried to run the executable I got a very odd message:

-bash: ./MOPAC2009.exe: No such file or directory

How odd is that? I could see the executable with ls and I even tabbed my way to the full filename. Unrelated to the actual error message, it turns out, that the MOPAC2009.exe executable is compiled as 32-bit, and not 64-bit as you'd expect in 2011, and it is actually here the problem lies - not a missing file! First, let me give the remedy, then my rant. You need to install a set of 32-bit compatible libraries that will let 32-bit code run. Since we're running Ubuntu, this is of course easy as pie!

sudo apt-get install ia32-libs

But very odd, that I get a complaint saying "No such file or directory"! Really! I was going nuts and had started blaming the NFS file system. A big thanks to my favorite web-site, where I found other possible causes for missing files, other than actual missing files.

However, the take home message of my post here really is: Start distributing source codes! I'm sure it's possible for users to compile Mopac (written in Fortran 90/95), if a decent Makefile is included. This would be the only drawback in distributing source codes. Another thing is, that binaries makes it impossible to optimize code at compiletime and link to optimal BLAS routines, etc.
 But these are not the big issues here. The things that really matter are: (1) You will never discover bugs from binaries and (2) you will never know what exact setting your algorithms are using from binaries. What if you needed to slightly tweak a parameter somewhere? What is you get unexpected results, and you know the input is good? You should of course turn to the source code! I have myself found bugs/quirks in both Dalton and GAMESS.

I had a Dalton calculation that kept crashing, and found a coefficient which was mis-typed. I quickly contacted the authors and things were solved SAME DAY, and the fix lives on in the new DALTON2011!
In GAMESS (and this is an even better story!) I discovered how to have semi-empirical methods run in parallel after snooping around in the code for a couple of days, looking for the Fock-matrix diagonalization routines. Turns out, that the only thing that prevents parallel semi-empirical methods is a flag in the code - remove that and voila!

Unfortunately cheerful stories as these will never happen to programs such as MOPAC. And I'm willing to bet money, that Mr. MOPAC would really prefer to receive a bug report along with a code patch, rather than just the bug reports.

MOPAC2009 is even free for academic use, and Jimmy Stewart (AKA Mr. MOPAC) has even been helpful in getting us started with a GAMESS implementation of his newest PM6 method. Thanks Jimmy!

UPDATE: Another thing concerning installation of MOPAC2009. The installation guide recommends using 777 permission for the installation. This is dangerous, and means that EVERYONE has write permissions to that folder. Stick with 755, which only gives the owner write permission - otherwise that file is an easy target for a hacker. 777 - bad, unless really necessary !

Tuesday, August 23, 2011

Debian Clusters for Education and Research: The Missing Manual

This is the recommendation of a great site I just found. I wish I had found it earlier! I have just been setting up a research/education cluster at Center for Molecular Computational Sciences at KU, with everything from NIS to NFS and Torque and even Sun Ray terminals.

This great site guides you through everything you need, in order to have a full, working cluster - only using free software. The author calls it "The Missing Manual", and I couldn't agree more!