diff --git a/src/sampleworks/utils/guidance_script_arguments.py b/src/sampleworks/utils/guidance_script_arguments.py
index 509c64ae..7e71449c 100644
--- a/src/sampleworks/utils/guidance_script_arguments.py
+++ b/src/sampleworks/utils/guidance_script_arguments.py
@@ -188,6 +188,12 @@ def populate_config_for_guidance_type(self, job: JobConfig, args: argparse.Names
             self.step_scaler_type = args.step_scaler_type
             self.ensemble_size = job.ensemble_size
 
+    def as_dict(self) -> dict[str, Any]:
+        """Return a dictionary representation of the guidance config, converting Path to strings"""
+        output = self.__dict__.copy()
+        output["density"] = str(self.density)
+        output["structure"] = str(self.structure)
+        return output
 
 def add_generic_args(parser: argparse.ArgumentParser | GuidanceConfig):
     parser.add_argument("--structure", type=str, required=True, help="Input structure")
diff --git a/src/sampleworks/utils/guidance_script_utils.py b/src/sampleworks/utils/guidance_script_utils.py
index bbefb327..b6b07ce5 100644
--- a/src/sampleworks/utils/guidance_script_utils.py
+++ b/src/sampleworks/utils/guidance_script_utils.py
@@ -594,7 +594,8 @@ def run_guidance_job_queue(job_queue_path: str) -> list[JobResult]:
         job_result = run_guidance(job, job.guidance_type, model_wrapper, device)
         # write out the job parameters to a JSON file in the same directory as the refined.cif file
         with open(Path(job_result.output_dir) / "job_metadata.json", "w") as fp:
-            json.dump(job.__dict__, fp)
+            # use the GuidanceConfig's as_dict() method to avoid serializing PosixPath objects
+            json.dump(job.as_dict(), fp)
 
         job_results.append(job_result)
         torch.cuda.empty_cache()  # just in case
diff --git a/src/sampleworks/utils/msa.py b/src/sampleworks/utils/msa.py
index 7647fbec..42f7dfbe 100644
--- a/src/sampleworks/utils/msa.py
+++ b/src/sampleworks/utils/msa.py
@@ -20,6 +20,85 @@
 MAX_MSA_SEQS = 16384
 
 
+def _validate_msa_cache_contents(msa_hash: str, msa_dir: Path) -> None:
+    """Validate the contents of the MSA cache.
+    For each hash, there should be a set of files `"{hash}_{n}.csv", "{hash}_{n}.a3m"`
+    where `n` is the index of the sequence in the input data, 0, 1, ..., len(sequences) - 1.
+    In most cases there is only one sequence, so `n = 0` only. The .a3m files should have a sequence
+    on every other line. The .csv files have two columns. The sequences in the second column of the
+    .csv file should be the same as the sequences in the a3m file.
+
+    Parameters
+    ----------
+    msa_hash : str
+        The hash key for the MSA files.
+    msa_dir : Path
+        The directory containing the MSA cache files.
+
+    Raises
+    ------
+    FileNotFoundError
+        If expected files are missing.
+    ValueError
+        If file contents don't match or are invalid.
+    """
+    # Find all files matching the hash pattern
+    csv_files = sorted(msa_dir.glob(f"{msa_hash}_*.csv"))
+    a3m_files = sorted(msa_dir.glob(f"{msa_hash}_*.a3m"))
+
+    if not csv_files:
+        raise FileNotFoundError(f"No CSV files found for hash {msa_hash} in {msa_dir}")
+    if not a3m_files:
+        raise FileNotFoundError(f"No A3M files found for hash {msa_hash} in {msa_dir}")
+
+    # Validate that we have matching pairs
+    csv_indices = {int(f.stem.split('_')[-1]) for f in csv_files}
+    a3m_indices = {int(f.stem.split('_')[-1]) for f in a3m_files}
+
+    if csv_indices != a3m_indices:
+        raise ValueError(
+            f"Mismatch between CSV and A3M file indices. "
+            f"CSV: {sorted(csv_indices)}, A3M: {sorted(a3m_indices)}"
+        )
+
+    # Validate each pair of files
+    for idx in sorted(csv_indices):
+        csv_path = msa_dir / f"{msa_hash}_{idx}.csv"
+        a3m_path = msa_dir / f"{msa_hash}_{idx}.a3m"
+
+        # Read CSV sequences (skip header, take second column)
+        with csv_path.open('r') as f:
+            csv_lines = f.readlines()
+
+        if not csv_lines or csv_lines[0].strip() != "key,sequence":
+            raise ValueError(f"Invalid CSV header in {csv_path}")
+
+        csv_sequences = [line.strip().split(',', 1)[1] for line in csv_lines[1:] if line.strip()]
+
+        # Read A3M sequences (every other line, skipping headers)
+        with a3m_path.open('r') as f:
+            a3m_lines = f.readlines()
+
+        # A3M format: header lines start with '>', sequences on alternating lines
+        a3m_sequences = [line.strip() for i, line in enumerate(a3m_lines) if i % 2 == 1]
+
+        # Validate that sequences match
+        if len(csv_sequences) != len(a3m_sequences):
+            raise ValueError(
+                f"Sequence count mismatch for index {idx}: "
+                f"CSV has {len(csv_sequences)}, A3M has {len(a3m_sequences)}"
+            )
+
+        for seq_idx, (csv_seq, a3m_seq) in enumerate(zip(csv_sequences, a3m_sequences)):
+            if csv_seq != a3m_seq:
+                raise ValueError(
+                    f"Sequence mismatch at index {idx}, sequence {seq_idx}: "
+                    f"CSV and A3M sequences differ"
+                )
+
+        logger.debug(f"Validated {len(csv_sequences)} sequences for hash {msa_hash}, index {idx}")
+
+
 # From https://github.com/jwohlwend/boltz/blob/main/src/boltz/main.py#L415
 # FIXME: this function is a big, long mess. We should clean it up.
 #   For the love of decent code, don't copy this and use it somewhere else and respect the
@@ -182,6 +261,7 @@ def _compute_msa(
         etc...
         """
 
+        # TODO I don't think these are actually used anywhere. We can probably remove them.
         msa_idx_idr = msa_dir / f"{idx}"
         msa_idx_idr.mkdir(exist_ok=True, parents=True)
         logger.info(f"Writing MSA for target {target_id} sequence {idx} to {msa_idx_idr.resolve()}")
@@ -270,7 +350,9 @@ def get_msa(
 
             if not all([m.exists() for m in msa_path_dict.values()]):
                 # this will generate both a3m and csv files for us.
-                msa_path_dict = _compute_msa(
+                # do NOT capture this output, it will return paths to .csv files,
+                # which we don't necessarily want.
+                _ = _compute_msa(
                     data,
                     hash_key,  # this is the "target_id" argument to compute_msa
                     self.msa_dir,
@@ -285,9 +367,11 @@ def get_msa(
             else:
                 self._cache_hits += 1
 
+            _validate_msa_cache_contents(hash_key, self.msa_dir)
+
         # Protenix needs special MSAs
         # This is a kind of hacky way to handle Protenix; I'm not sure what to do easily but
-        # use their pipeline, and just have it put everything in our cache directory.
+        # use their pipeline and just have it put everything in our cache directory.
         elif structure_predictor == StructurePredictor.PROTENIX:
             if not PROTENIX_AVAILABLE:
                 raise RuntimeError("Protenix is not installed, cannot use Protenix MSA tools.")