MT-REXEE development

.github/workflows/lint.yaml

@@ -2,6 +2,7 @@ name: lint
 
 on:
   push:
+  pull_request:
 
 jobs:
 

README.md

@@ -12,16 +12,21 @@ Ensemble Molecular Dynamics
 [![Downloads](https://static.pepy.tech/badge/ensemble-md)](https://pepy.tech/project/ensemble-md)
 
 
-`ensemble_md` is a Python package that provides methods for setting up, running, and analyzing GROMACS simulation ensembles. Currently, the package implements all the necessary algorithms for running synchronous replica exchange (REX) of expanded ensembles (EE), abbreviated as REXEE, as well as its multi-topology (MT) variation, MT-REXEE. Our future work includes implementing asynchronous REXEE and other possible variations of the REXEE method. For installation instructions, theory overview, tutorials, and API references, please visit the [documentation](https://ensemble-md.readthedocs.io/en/latest/?badge=latest) and our [JCTC paper](https://pubs.acs.org/doi/epdf/10.1021/acs.jctc.4c00484).
+`ensemble_md` is a Python package that provides methods for setting up, running, and analyzing GROMACS simulation ensembles. Currently, the package implements all the necessary algorithms for running synchronous replica exchange (REX) of expanded ensembles (EE), abbreviated as REXEE, as well as its multi-topology (MT) variation, MT-REXEE. Our future work includes implementing asynchronous REXEE and other possible variations of the REXEE method. For installation instructions, theory overview, tutorials, and API references, please visit the [documentation](https://ensemble-md.readthedocs.io/en/latest/?badge=latest) and our papers (listed in the next section).
 
-### Reference
+### References
 If you use any components of the Python package `ensemble_md` or the REXEE method in your research, please cite the following paper:
 
-Hsu, W. T., & Shirts, M. R. (2024). Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability. *Journal of Chemical Theory and Computation*. 20.14 (2024): 6062-6081. doi: [10.1021/acs.jctc.4c00484](https://doi.org/10.1021/acs.jctc.4c00484)
+> Hsu, W. T., & Shirts, M. R. (2024). Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability. *Journal of Chemical Theory and Computation*. 20.14 (2024): 6062-6081. doi: [10.1021/acs.jctc.4c00484](https://doi.org/10.1021/acs.jctc.4c00484)
 
-### Copyright
+If you use the MT-REXEE method in your research, please cite the following paper:
 
-Copyright (c) 2022, Wei-Tse Hsu
+> Friedman, A. J., Hsu, W. T., & Shirts, M. R. (2024). Multiple Topology Replica Exchange of Expanded Ensembles (MT-REXEE) for Multidimensional Alchemical Calculations. arXiv preprint arXiv:2408.11038. doi: [10.48550/arXiv.2408.11038](
+https://doi.org/10.48550/arXiv.2408.11038)
+
+### Authors
+- Wei-Tse Hsu, University of Colorado, Boulder ([email protected])
+- Anika Friedman, University of Colorado, Boulder ([email protected])
 
 
 ### Acknowledgements

ensemble_md/cli/run_REXEE.py

@@ -99,6 +99,8 @@ def main():
                 os.mkdir(f'{REXEE.working_dir}/sim_{i}/iteration_0')
                 MDP = REXEE.initialize_MDP(i)
                 MDP.write(f"{REXEE.working_dir}/sim_{i}/iteration_0/expanded.mdp", skipempty=True)
+            if REXEE.modify_coords == 'default' and (not os.path.exists('residue_connect.csv') or not os.path.exists('residue_swap_map.csv')):  # noqa: E501
+                REXEE.process_top()
 
         # 2-2. Run the first set of simulations
         REXEE.run_REXEE(0)

ensemble_md/replica_exchange_EE.py

@@ -29,6 +29,7 @@
 import ensemble_md
 from ensemble_md.utils import utils
 from ensemble_md.utils import gmx_parser
+from ensemble_md.utils import coordinate_swap
 from ensemble_md.utils.exceptions import ParameterError
 
 comm = MPI.COMM_WORLD
@@ -157,6 +158,8 @@ def set_params(self, analysis):
         optional_args = {
             "add_swappables": None,
             "modify_coords": None,
+            "resname_list": None,
+            "swap_rep_pattern": None,
             "nst_sim": None,
             "proposal": 'exhaustive',
             "w_combine": False,
@@ -254,6 +257,10 @@ def set_params(self, analysis):
                 raise ParameterError(f"The parameter '{i}' should be a boolean variable.")
 
         params_list = ['add_swappables', 'df_ref']
+        if self.resname_list is not None:
+            params_list.append('resname_list')
+        if self.swap_rep_pattern is not None:
+            params_list.append('swap_rep_pattern')
         for i in params_list:
             if getattr(self, i) is not None and not isinstance(getattr(self, i), list):
                 raise ParameterError(f"The parameter '{i}' should be a list.")
@@ -441,17 +448,24 @@ def set_params(self, analysis):
 
         # 7-12. External module for coordinate modification
         if self.modify_coords is not None:
-            module_file = os.path.basename(self.modify_coords)
-            module_dir = os.path.dirname(self.modify_coords)
-            if module_dir not in sys.path:
-                sys.path.append(module_dir)  # so that the module can be imported
-            module_name = os.path.splitext(module_file)[0]
-            module = importlib.import_module(module_name)
-            if not hasattr(module, module_name):
-                err_msg = f'The module for coordinate manipulation (specified through the parameter) must have a function with the same name as the module, i.e., {module_name}.'  # noqa: E501
-                raise ParameterError(err_msg)
+            if self.modify_coords == 'default':
+                if self.swap_rep_pattern is None and (not os.path.exists('residue_connect.csv') or not os.path.exists('residue_swap_map.csv')):  # noqa: E501
+                    raise Exception('swap_rep_pattern option must be filled in if using default swapping function and not swap guide')  # noqa: E501
+                if self.resname_list is None and (not os.path.exists('residue_connect.csv') or not os.path.exists('residue_swap_map.csv')):  # noqa: E501
+                    raise Exception('resname_list option must be filled in if using default swapping function and not swap guide')  # noqa: E501
+                self.modify_coords_fn = self.default_coords_fn
             else:
-                self.modify_coords_fn = getattr(module, module_name)
+                module_file = os.path.basename(self.modify_coords)
+                module_dir = os.path.dirname(self.modify_coords)
+                if module_dir not in sys.path:
+                    sys.path.append(module_dir)  # so that the module can be imported
+                module_name = os.path.splitext(module_file)[0]
+                module = importlib.import_module(module_name)
+                if not hasattr(module, module_name):
+                    err_msg = f'The module for coordinate manipulation (specified through the parameter) must have a function with the same name as the module, i.e., {module_name}.'  # noqa: E501
+                    raise ParameterError(err_msg)
+                else:
+                    self.modify_coords_fn = getattr(module, module_name)
         else:
             self.modify_coords_fn = None
 
@@ -1496,3 +1510,143 @@ def run_REXEE(self, n, swap_pattern=None):
         # want it to start parsing the dhdl file (in the if condition of if rank == 0) of simulation 3 being run by
         # rank 3 that has not been generated, which will lead to an I/O error.
         comm.barrier()
+
+    def default_coords_fn(self, molA_file_name: str, molB_file_name: str):
+        """
+        Swap coordinates between two GRO files
+
+        Parameters
+        ----------
+        molA_file_name : str
+            GRO file name for the moleucle to be swapped
+        molB_file_name : str
+            GRO file name for the other moleucle to be swapped
+        Return
+        ------
+            None
+        """
+        # Step 1: Load necessary files
+        import mdtraj as md
+        import pandas as pd
+
+        # Determine name for transformed residue
+        molA_dir = molA_file_name.rsplit('/', 1)[0] + '/'
+        molB_dir = molB_file_name.rsplit('/', 1)[0] + '/'
+
+        # Load trajectory trr for higher precison coordinates
+        molA = md.load_trr(f'{molA_dir}/traj.trr', top=molA_file_name).slice(-1)  # Load last frame of trr trajectory
+        molB = md.load_trr(f'{molB_dir}/traj.trr', top=molB_file_name).slice(-1)
+
+        # Load the coordinate swapping map
+        connection_map = pd.read_csv('residue_connect.csv')
+        swap_map = pd.read_csv('residue_swap_map.csv')
+
+        # Step 2: Read the GRO input coordinate files and open temporary Output files
+        molA_file = open(molA_file_name, 'r').readlines()  # open input file
+        molB_new_file_name = 'B_hybrid_swap.gro'
+        molB_new = open(molB_new_file_name, 'w')
+        molB_file = open(molB_file_name, 'r').readlines()  # open input file
+        molA_new_file_name = 'A_hybrid_swap.gro'
+        molA_new = open(molA_new_file_name, 'w')
+
+        # Step 3: Determine atoms for alignment and swapping
+        nameA = coordinate_swap.identify_res(molA.topology, swap_map['Swap A'].to_list() + swap_map['Swap B'].to_list())  # noqa: E501
+        nameB = coordinate_swap.identify_res(molB.topology, swap_map['Swap A'].to_list() + swap_map['Swap B'].to_list())  # noqa: E501
+        df_atom_swap = coordinate_swap.find_common(molA_file, molB_file, nameA, nameB)
+
+        # Step 4: Fix break if present for solvated systems only
+        if len(molA.topology.select('water')) != 0:
+            A_dimensions = coordinate_swap.get_dimensions(molA_file)
+            B_dimensions = coordinate_swap.get_dimensions(molB_file)
+            molA = coordinate_swap.fix_break(molA, nameA, A_dimensions, connection_map[connection_map['Resname'] == nameA])  # noqa: E501
+            molB = coordinate_swap.fix_break(molB, nameB, B_dimensions, connection_map[connection_map['Resname'] == nameB])  # noqa: E501
+
+        # Step 5: Determine coordinates of atoms which need to be reconstructed as we swap coordinates between molecules  # noqa: E501
+        miss_B = df_atom_swap[(df_atom_swap['Swap'] == 'B2A') & (df_atom_swap['Direction'] == 'miss')]['Name'].to_list()  # noqa: E501
+        miss_A = df_atom_swap[(df_atom_swap['Swap'] == 'A2B') & (df_atom_swap['Direction'] == 'miss')]['Name'].to_list()  # noqa: E501
+        if len(miss_B) != 0:
+            df_atom_swap = coordinate_swap.get_miss_coord(molB, molA, nameB, nameA, df_atom_swap, 'B2A', swap_map[(swap_map['Swap A'] == nameB) & (swap_map['Swap B'] == nameA)])  # noqa: E501
+        if len(miss_A) != 0:
+            df_atom_swap = coordinate_swap.get_miss_coord(molA, molB, nameA, nameB, df_atom_swap, 'A2B', swap_map[(swap_map['Swap A'] == nameA) & (swap_map['Swap B'] == nameB)])  # noqa: E501
+
+        # Reprint preamble text
+        line_start = coordinate_swap.print_preamble(molA_file, molB_new, len(miss_B), len(miss_A))
+
+        # Print new coordinates to file for molB
+        coordinate_swap.write_new_file(df_atom_swap, 'A2B', 'B2A', line_start, molA_file, molB_new, nameA, nameB, copy.deepcopy(molA.xyz[0]), miss_A)  # noqa: E501
+
+        # Print new coordinates to file
+        # Reprint preamble text
+        line_start = coordinate_swap.print_preamble(molB_file, molA_new, len(miss_A), len(miss_B))
+
+        # Print new coordinates for molA
+        coordinate_swap.write_new_file(df_atom_swap, 'B2A', 'A2B', line_start, molB_file, molA_new, nameB, nameA, copy.deepcopy(molB.xyz[0]), miss_B)  # noqa: E501
+
+        # Rename temp files
+        os.rename('A_hybrid_swap.gro', molB_dir + '/confout.gro')
+        os.rename('B_hybrid_swap.gro', molA_dir + '/confout.gro')
+
+    def process_top(self):
+        """
+        Process the input topologies in order to determine the atoms for alignment in the default GRO swapping
+        function. Output as csv files to prevent needing to re-run this step.
+
+        Parameters
+        ----------
+            None
+        Return
+        ------
+            None
+        """
+        import pandas as pd
+
+        if not os.path.exists('residue_connect.csv'):
+            df_top = pd.DataFrame()
+            for f, file_name in enumerate(self.top):
+                # Read file
+                input_file = coordinate_swap.read_top(file_name, self.resname_list[f])
+
+                # Determine the atom names corresponding to the atom numbers
+                start_line, atom_name, state = coordinate_swap.get_names(input_file)
+
+                # Determine the connectivity of all atoms
+                connect_1, connect_2, state_1, state_2 = [], [], [], []  # Atom 1 and atom 2 which are connected and which state they are dummy atoms  # noqa: E501
+                for l, line in enumerate(input_file[start_line:]):  # noqa: E741
+                    line_sep = line.split(' ')
+                    if line_sep[0] == ';':
+                        continue
+                    if line_sep[0] == '\n':
+                        break
+                    while '' in line_sep:
+                        line_sep.remove('')
+                    connect_1.append(atom_name[int(line_sep[0])-1])
+                    connect_2.append(atom_name[int(line_sep[1])-1])
+                    state_1.append(state[int(line_sep[0])-1])
+                    state_2.append(state[int(line_sep[1])-1])
+                df = pd.DataFrame({'Resname': self.resname_list[f], 'Connect 1': connect_1, 'Connect 2': connect_2, 'State 1': state_1, 'State 2': state_2})  # noqa: E501
+                df_top = pd.concat([df_top, df])
+            df_top.to_csv('residue_connect.csv')
+        else:
+            df_top = pd.read_csv('residue_connect.csv')
+
+        if not os.path.exists('residue_swap_map.csv'):
+            df_map = pd.DataFrame()
+            for swap in self.swap_rep_pattern:
+                # Determine atoms not present in both molecules
+                X, Y = [int(swap[0][0]), int(swap[1][0])]
+                lam = {X: int(swap[0][1]), Y: int(swap[1][1])}
+                for A, B in zip([X, Y], [Y, X]):
+                    input_A = coordinate_swap.read_top(self.top[A], self.resname_list[A])
+                    start_line, A_name, state = coordinate_swap.get_names(input_A)
+                    input_B = coordinate_swap.read_top(self.top[B], self.resname_list[B])
+                    start_line, B_name, state = coordinate_swap.get_names(input_B)
+
+                    A_only = [x for x in A_name if x not in B_name]
+                    B_only = [x for x in B_name if x not in A_name]
+
+                    # Seperate real to dummy switches
+                    df = coordinate_swap.determine_connection(A_only, B_only, self.resname_list[A], self.resname_list[B], df_top, lam[A])  # noqa: E501
+
+                    df_map = pd.concat([df_map, df])
+
+            df_map.to_csv('residue_swap_map.csv')