DOI: 10.5061/dryad.n5tb2rc1c
title: Data from: In vivo functional phenotypes from a computational epistatic model of evolution

description: Computational models of evolution are valuable for understanding the
 dynamics of sequence variation, to infer phylogenetic relationships or
 potential evolutionary pathways, and for biomedical and industrial
 applications. Despite these benefits, few have validated their
 propensities to generate outputs with in vivo functionality, which would
 enhance their value as accurate and interpretable evolutionary algorithms.
 Utilizing the Hamiltonian of the joint probability of sequences in the
 family as fitness metric, we sampled and experimentally tested for in vivo
 beta-lactamase activity in E. coli TEM-1 variants.  These
 variants retain family-like functionality while being more active than
 their WT predecessor. We found that depending on the inference method used
 to generate the epistatic constraints, different parameters simulate
 diverse selection strengths. Under weaker selection, local Hamiltonian
 fluctuations reliably predict relative changes to variant fitness,
 recapitulating neutral evolution. In this dataset, we include input
 datasets, simulation trajectories as well as experimental data to support
 the publication: "In vivo functional phenotypes from a computationa
 epistatic model of evolution".
description: Direct coupling analysis methods used were mean field
 (https://github.com/morcoslab/SEEC-nt) or Boltzman machine learning (https://github.com/matteofigliuzzi/bmDCA) SI Dataset S4 (PhaseI_MSA.fasta)<br>Phase I multiple sequence alignment used for SEEC-AA mfDCA and bmDCA statistical inference was obtained from Pfam and pre-processed to remove sequences with more than 5% consecuitve gaps.  SI Dataset S5 (PhaseII_MSA.fasta)<br>Phase II multiple sequence alignment used for SEEC-NT mfDCA and bmDCA statistical inference was generated using HMMTools with TEM-1 sequence as seed  (excluding signal petide) and default parameters. SI Dataset S6 (PhaseII_mfDCA_Parameters.mat) 1. PhaseII_mfDCA_eij (size=5523x5523)<br>2. PhaseII_mfDCA_hi (size=21x263)<br>Coupling and local field matrices inferred using mean field DCA with the PhaseII MSA as the input. This file is a .mat readable in Matlab and compatible with the code found at Github (github.com/morcoslab/SEEC-NT) SI Dataset S7 (PhaseII_bmDCA_Parameters.mat) 1. eij (size=5523x5523)<br>2. hi (size=21x263)<br>Coupling (eij) and local field (hi) matrices inferred using Boltzmann machine learning DCA with the PhaseII MSA as the input.  The eij matrices have been converted into the format that matches the output of mfDCA. This file is a .mat readable in Matlab and compatible with the code found at Github (github.com/morcoslab/SEEC-NT) SI Dataset S8 (SEEC_nt_sequence_trajectories.mat) Variables: 1. SEEC_nt_bmDCA_Trajectory_amino_T0_75_3 (size=5000x263)<br>2. SEEC_nt_mfDCA_Trajectory_amino_T1_5_1 (size=5000x263)<br>Sequences output from SEEC-nt used for variant selection. A .mat file readable in Matlab.  SI Dataset S9 (Figure_3_Sanger_Sequencing_Data.zip),SI Dataset S10 (Figure_4_Sanger_Sequencing_Data.zip)<br>Raw Sanger Sequencing chromatograms collected from plasmid samples isolated from assay cultures.  Naming of chromatograms is as follows:<br>first number refers to the batch of sequencing<br>second number is the sample run within that batch<br>"for" or "rev" refers to the sequencing forward or reverse directions, respectively.<br>The rest of the name comes from the variant name as used in the manuscript, where Beg, Mid or Late refer to positions in the simulation trajectory, bm or mf refer to the DCA implementation used to infer the coupling and local field parameters, the number is the variant number, and NT indicates the algorithm used was SEEC-nucleotide.<br>Sanger sequencing Chromatograms can be viewed using free software such as 4peaks (https://nucleobytes.com/4peaks/), Benchling (https://www.benchling.com/), or a number of other platforms.
description: .mat files must be opened using Matlab Sanger
 sequencing data can be viewed using 4peaks
 (https://nucleobytes.com/4peaks/), Benchling (https://www.benchling.com/),
 or a number of other platforms. Fasta files can be read
 using Matlab, bioPython or any Multiple Sequence Alignment visualization
 software. 
description: # Data from: In vivo functional phenotypes from a computational epistatic
 model of evolution --- This dataset includes sequence data, model
 parameters, similation trajectories and experimental data for Sanger
 sequecing related to a model of sequence evolution called Sequence
 Evolution with Epistatic Contributions (SEEC) applied to beta-lactamase
 TEM-1. ## Description of the data and file structure SI Dataset S4
 (PhaseI_MSA.fasta)Phase I multiple sequence alignment used for SEEC-AA
 mfDCA and bmDCA statistical inference was obtained from Pfam and
 pre-processed to remove sequences with more than 5% consecuitve gaps. SI
 Dataset S5 (PhaseII_MSA.fasta)Phase II multiple sequence alignment used
 for SEEC-NT mfDCA and bmDCA statistical inference was generated using
 HMMTools with TEM-1 sequence as seed (excluding signal petide) and default
 parameters. SI Dataset S6 (PhaseII_mfDCA_Parameters.mat)Coupling and local
 field matrices inferred using mean field DCA with the PhaseII MSA as the
 input. Objects: 1. PhaseII_mfDCA_eij (size=5523x5523) 2. PhaseII_mfDCA_hi
 (size=21x263) This file is a .mat readable in Matlab and compatible with
 the code found at Github (github.com/morcoslab/SEEC-NT) SI Dataset S7
 (PhaseII_bmDCA_Parameters.mat) Objects: 1. eij (size=5523x5523) 2. hi
 (size=21x263) Coupling (eij) and local field (hi) matrices inferred using
 Boltzmann machine learning DCA with the PhaseII MSA as the input. The eij
 matrices have been converted into the format that matches the output of
 mfDCA. This file is a .mat readable in Matlab and compatible with the code
 found at Github (github.com/morcoslab/SEEC-NT) SI Dataset S8
 (SEEC_nt_sequence_trajectories.mat) Objects: 1.
 SEEC_nt_bmDCA_Trajectory_amino_T0_75_3 (size=5000x263) 2.
 SEEC_nt_mfDCA_Trajectory_amino_T1_5_1 (size=5000x263) Sequences output
 from SEEC-nt used for variant selection. A .mat file readable in Matlab.
 SI Dataset S9 (Figure_3_Sanger_Sequencing_Data.zip),SI Dataset S10
 (Figure_4_Sanger_Sequencing_Data.zip) Raw Sanger Sequencing chromatograms
 collected from plasmid samples isolated from assay cultures. Naming of
 chromatograms is as follows: First number refers to the batch of
 sequencing. Second number is the sample run within that batch.
 "For" or "rev" refers to the sequencing forward or
 reverse directions, respectively. The rest of the name comes from the
 variant name as used in the manuscript, where Beg, Mid or Late refer to
 positions in the simulation trajectory, bm or mf refer to the DCA
 implementation used to infer the coupling and local field parameters, the
 number is the variant number, and NT indicates the algorithm used was
 SEEC-nucleotide. Sanger sequencing Chromatograms can be viewed using free
 software such as 4peaks (),
 Benchling (), or a number of other
 platforms. ## Code/Software Code and scripts used to generate the data in
 this repository can be found at
 The Boltzman machine DCA
 (bmDCA) implementation used can be found at