Predictions of side-chain χ angles as well as the final, per-residue accuracy of the structure (pLDDT) are computed with small per-residue networks on the final activations at the end of the network. The estimate of the TM-score (pTM) is obtained from a pairwise error prediction that is computed as a linear projection from the final pair representation. The final loss (which we term the frame-aligned point error (FAPE) (Fig. 3f)) compares the predicted atom positions to the true positions under many different alignments. For each alignment, defined by aligning the predicted frame (Rk, tk) to the corresponding true frame, we compute the distance of all predicted atom positions xi from the true atom positions. The resulting Nframes Natoms distances are penalized with a clamped L1 loss. This creates a strong bias for atoms to be correct relative to the local frame of each residue and hence correct with respect to its side-chain interactions, as well as providing the main source of chirality for AlphaFold (Supplementary Methods 1.9.3 and Supplementary Fig. 9).
Additionally, we randomly mask out or mutate individual residues within the MSA and have a Bidirectional Encoder Representations from Transformers (BERT)-style37 objective to predict the masked elements of the MSA sequences. This objective encourages the network to learn to interpret phylogenetic and covariation relationships without hardcoding a particular correlation statistic into the features. The BERT objective is trained jointly with the normal PDB structure loss on the same training examples and is not pre-trained, in contrast to recent independent work38.
AMBER 16 Source Code
To quantify the effect of the different sequence data sources, we re-ran the CASP14 proteins using the same models but varying how the MSA was constructed. Removing BFD reduced the mean accuracy by 0.4 GDT, removing Mgnify reduced the mean accuracy by 0.7 GDT, and removing both reduced the mean accuracy by 6.1 GDT. In each case, we found that most targets had very small changes in accuracy but a few outliers had very large (20+ GDT) differences. This is consistent with the results in Fig. 5a in which the depth of the MSA is relatively unimportant until it approaches a threshold value of around 30 sequences when the MSA size effects become quite large. We observe mostly overlapping effects between inclusion of BFD and Mgnify, but having at least one of these metagenomics databases is very important for target classes that are poorly represented in UniRef, and having both was necessary to achieve full CASP accuracy.
Using our CASP14 configuration for AlphaFold, the trunk of the network is run multiple times with different random choices for the MSA cluster centres (see Supplementary Methods 1.11.2 for details of the ensembling procedure). The full time to make a structure prediction varies considerably depending on the length of the protein. Representative timings for the neural network using a single model on V100 GPU are 4.8 min with 256 residues, 9.2 min with 384 residues and 18 h at 2,500 residues. These timings are measured using our open-source code, and the open-source code is notably faster than the version we ran in CASP14 as we now use the XLA compiler75.
Data analysis used Python v.3.6 ( ), NumPy v.1.16.4 ( ), SciPy v.1.2.1 ( ), seaborn v.0.11.1 ( ), Matplotlib v.3.3.4 ( ), bokeh v.1.4.0 ( ), pandas v.1.1.5 ( -dev/pandas), plotnine v.0.8.0 ( ), statsmodels v.0.12.2 ( ) and Colab ( ). TM-align v.20190822 ( -align/) was used for computing TM-scores. Structure visualizations were created in Pymol v.2.3.0 ( -open-source).
These are instructions for compiling a variety of atomistic codes. By atomistic codes, we include codes that simulate the behavior of particles such as LAMMPS, codes for Classical Molecular Dynamics (CMD) such as AMBER, GROMACS, and NAMD,Tight Binding codes such as DFTB+ and DFT codes such as ABINIT, OCTOPUS, VASP, and Quantum Espresso.
A set of numerical libraries are used very often by these codes. Dense Linear Algebra routines such as BLAS and LAPACK are used. Optimized versions such as OpenBLAS and Intel MKL are preferable over the reference versions from NetLIB.
All atomistic codes in our list take advantage of parallelization, either OpenMP, MPI, or support GPUs. OpenMP is implemented on modern compilers such as GCC, Intel, and NVIDIA. For MPI we will use MPICH 3.4.1, OpenMPI 3.1.6, and Intel MPI from 2019.
Other libraries needed for compiling these codes include an FFT library such as FFTW 3.3.9 or the implementation in MKL. The libraries need to be compiled for single and double precision as some codes use both. An HDF5 and NetCDF librariesprovide hierarchical data storage for numerical data. Finally, a Python implementation is often needed, because these codes include a Python interface or use it for building or testing.
In this document, we provide instructions to compile Amber using GCC 9.3, 11.1, and Intel Compilers 2021Amber can be compiled with a variety of options for parallelization.Amber can be compiled as pure serial code, using multithreading with OpenMP, distributed parallelism with MPI, and using GPUs with CUDA.We will be building Amber with each option and one final compilation enabling OpenMP, MPI, and CUDA.
Amber as a package is composed of two pieces, Amber itself and AmberTools.Amber facilitates faster simulations (on parallel CPU or GPU hardware) and is distributed with a paid license.AmberTools is a free package that collect open-source code to be used in conjunction with Amber.The version used to produce these notes is Amber 20, the latest version available by mid-2021.
Decompressing Amber20.tar.bz2 and AmberTools20.tar.bz2 will create a folder amber20_src.There is one file amber20_src/build/run_cmake.sample that we will use as a template changing it for each build from now on.It is always convenient to build the code on a folder separated from the sources.Create a folder for each build, for the serial case, we suggest:
The reason for the name is that in this folder we will compile all builds, including the MPI version using MPICH 3.4.1Inside this folder copy the file amber20_src/build/run_cmake.sampleThe orginal content of this file is:
Now we need to load some modules for compiling the code.Amber uses CMAKE as a software builder.The version included with RedHat 7.x (2.18) is too old for most scientific codes.We will load modules for cmake 3.21.1 and GCC 9.3:
If you do skip this step, conda will remove pip after the update and several other python packages that are installed with pip will fail.This is the only change in the sources.No other changes will be done directly to sources, if something fails, we simply disable the corresponding package.Run the run_cmake inside the corresponding build folder:
To run the testsuite, the modulefile needs to be created and loaded. The module must set the variable $AMBERHOME needed to run the tests.Go to the folder amber20_src/test that contains the tests.For the parallel tests, set the variable $DO_PARALLEL to the right command for running MPI executions, for example:
An Amber Alert (alternatively styled AMBER alert) or a child abduction emergency alert (SAME code: CAE) is a message distributed by a child abduction alert system to ask the public for help in finding abducted children.[1][2] The system originated in the United States.[1]
"Amber" refers to two things: a set of molecular mechanical force fields for the simulation of biomolecules (which are in the public domain, and are used in a variety of simulation programs); and a package of molecular simulation programs which includes source code and demos. This installation contains both of the above.
MM-PBSA is a post-processing end-state method to calculate free energies of molecules in solution. MMPBSA.py is a program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics (MD) or Monte Carlo (MC) simulations. Several implicit solvation models are available with MMPBSA.py, including the Poisson-Boltzmann Model, the Generalized Born Model, and the Reference Interaction Site Model. Vibrational frequencies may be calculated using normal mode or quasi-harmonic analysis to approximate the solute entropy. Specific interactions can also be dissected using free energy decomposition or alanine scanning. A parallel implementation significantly speeds up the calculation by dividing frames evenly across available processors. MMPBSA.py is an efficient, user-friendly program with the flexibility to accommodate the needs of users performing end-state free energy calculations. The source code can be downloaded at with AmberTools, released under the GNU General Public License.
#!/bin/bash #SBATCH -n 8 #SBATCH -t 40:00:00 #SBATCH -A SNIC2014-11-32 module add amber/14 srun --cpu_bind=rank pmemd.MPI -O -i prot-0.00-sander.in1 -o prot-0.00-sander.out1 -p prot.prm -c prot.rst -r prot-0.00.mdrest1 -ref prot.rst srun --cpu_bind=rank pmemd.MPI -O -i prot-0.00-sander.in2 -o prot-0.00-sander.out2 -p prot.prm -c prot-0.00.mdrest1 -r prot-0.00.mdrest2 -ref prot.rst srun --cpu_bind=rank pmemd.MPI -O -i prot-0.00-sander.in3 -o prot-0.00-sander.out3 -p prot.prm -c prot-0.00.mdrest2 -r prot-0.00.mdrest3 srun --cpu_bind=rank pmemd.MPI -O -i prot-0.00-sander.in4 -o prot-0.00-sander.out4 -p prot.prm -c prot-0.00.mdrest3 -r prot-0.00.mdrest4 -x prot-0.00.mdcrd4
Sarek: setenv AMBERHOME /kfs/home/t/throd/amber/amber8 Serial sander: > module load pgi-compiler/5.2-4 > sander Parallel sander: > module load mpich mpich/1.2.5..12/gm/pgi > module load pgi-compiler/5.2-4 > mpirun -np mpi/sander Parallel pmemd: > module load mpich/1.2.5..12/gm/pgi > module load gm/2.1.2 (maybe this is not necesary) > module load pgi-compiler/5.2-4 > mpirun -np mpi/pmemd (or psander) Seth: setenv AMBERHOME /kfs/home/t/throd/amber/amber8_seth > module load intel-compiler/8.1 Serial sander: > sander Parallel sander: > mpirun -np mpi/sander (or psander) Docenten: setenv AMBERHOME /sw/pkg/bio/Amber8 > module load intel/8.1 Serial sander: > sander Parallel sander: > module load mpich-intel8/1.2.5.2 > mpirun -np mpi/sander (or psander) Sigrid: Amber is compiled with static libraries, so it should work on all the swegrid clusters setenv AMBERHOME /sw/pkg/bio/Amber8 Serial sander: > sander Parallel sander: > module load mpich-intel8/1.2.5.2 > mpirun -np mpi/sander (or psander) toto7/whemim64: . use_modules module load intel/8.1 export AMBERHOME=/sw/amber/Amber8 export PATH=$AMBERHOME/exe:$PATH Locally: exportLD_LIBRARY_PATH=/opt/intel/f_compiler81/lib:/opt/intel/cc_compiler81/lib:$LD_LIBRARY_PATH export AMBERHOME=/home/bio/AMBER/Amber8 setenv LD_LIBRARY_PATH /opt/intel/f_compiler81/lib:/opt/intel/cc_compiler81/lib:$LD_LIBRARY_PATH setenv AMBERHOME /home/bio/AMBER/Amber8 2ff7e9595c
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