| \documentclass{article} |
| \usepackage[utf8]{inputenc} |
| \usepackage{booktabs} |
| \usepackage{multirow} |
| \usepackage{graphicx} |
| \usepackage{amsmath} |
| \usepackage{array} |
| \usepackage{xcolor} |
| \usepackage{colortbl} |
| \usepackage{pgfplots} |
| \usepackage{tikz} |
| \usepackage{longtable} |
| \pgfplotsset{compat=1.17} |
|
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| \title{Supplementary Data: CFG-Enhanced Flow Matching for AMP Generation} |
| \date{\today} |
|
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| \begin{document} |
|
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| \maketitle |
|
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| \section{Detailed Sequence Data} |
|
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| \subsection{Complete APEX Results - Top 10 Candidates} |
|
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| \begin{longtable}{@{}p{0.15\textwidth}p{0.1\textwidth}p{0.6\textwidth}p{0.08\textwidth}@{}} |
| \toprule |
| \textbf{Rank} & \textbf{MIC (μg/mL)} & \textbf{Sequence} & \textbf{CFG} \\ |
| \midrule |
| \endhead |
| 1 & 236.43 & VLLVFLFLTLRIRAYLASAVRYRFLATIALLFILLAALLIIFILLVILVT & No CFG \\ |
| 2 & 239.89 & VLRIIAILLISLIATVALAITLTLRRVRLVSSLLAYISYELLIATDVSTL & Strong \\ |
| 3 & 248.15 & DITRVLIYAALALRSLITDTLSLLLVVVLRIILAIAALLISFARVAVSLL & Strong \\ |
| 4 & 250.13 & ALRATIRAILLTIASDIVLLVTILITLLAVEVDTIESTVVTELLRATAVV & No CFG \\ |
| 5 & 256.03 & LISILLIIAALSVSVVREALAYAADLRIEVSLTLLFILLYLIVVDLALSA & V. Strong \\ |
| 6 & 257.08 & DLLIREASTTLDLIVAVTVLSLLVLAETASLAIALLIIDELELVLIDLIT & Weak \\ |
| 7 & 257.54 & RDLLLTEIRARYIADRVTLFTEATTLLLSDLLLFLYATARAEITTEFIAL & V. Strong \\ |
| 8 & 257.56 & LVAILIAFFRILDRAEAILIEESDLFSSLSLTLIILIDIVAIIFLLLLLV & V. Strong \\ |
| 9 & 257.98 & VRELAIIALLIIITLLASARAIVFLRALDALVEISLFIALSRSVIIAVSS & Strong \\ |
| 10 & 259.33 & LLVARVTIFLESLLAFTALAILVLVLVLALLFIAFYFDTAFTTISTFLLA & No CFG \\ |
| \bottomrule |
| \caption{Complete APEX MIC Predictions - Top 10 Performing Sequences} |
| \end{longtable} |
|
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| \subsection{Complete HMD-AMP Results} |
|
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| \begin{longtable}{@{}p{0.2\textwidth}p{0.08\textwidth}p{0.08\textwidth}p{0.5\textwidth}p{0.06\textwidth}@{}} |
| \toprule |
| \textbf{Sequence ID} & \textbf{AMP Prob} & \textbf{Prediction} & \textbf{Sequence} & \textbf{Cationic} \\ |
| \midrule |
| \endhead |
| generated\_seq\_001 & 0.854 & AMP & LLEVRDAELAIAVFLTTALAIILARLFTIALETSLLATLAVLLFARLYVS & 3 \\ |
| generated\_seq\_002 & 0.380 & Non-AMP & AELVLRIVAEATARSRVLFIIVIDVSVDDAELLLTALLIASLTRSTRVVS & 5 \\ |
| generated\_seq\_003 & 0.061 & Non-AMP & IIIAFRRRIAALLAVALATLSLVFAFEEDLLAEFSSYYSATFAALFDIAV & 3 \\ |
| generated\_seq\_004 & 0.663 & AMP & LVVLVAVVLAILVLILLLIFIFTIVAADLLDYTLEEIISARYLLIVLLLT & 1 \\ |
| generated\_seq\_005 & 0.209 & Non-AMP & ETYALLILEFTLLLLIIAYADTAFLAELRLAVAITASRLSLLSLTLIASE & 2 \\ |
| generated\_seq\_006 & 0.492 & Non-AMP & FAESTEALLALLALAFLFVLVLLESTRLALALLVLVFSTLVVAELLLVLI & 3 \\ |
| generated\_seq\_007 & 0.209 & Non-AMP & VRELAIIALLIIITLLASARAIVFLRALDALVEISLFIALSRSVIIAVSS & 4 \\ |
| generated\_seq\_008 & 0.246 & Non-AMP & VLRIIAILLISLIATVALAITLTLRRVRLVSSLLAYISYELLIATDVSTL & 1 \\ |
| generated\_seq\_009 & 0.319 & Non-AMP & LLVARVTIFLESLLAFTALAILVLVLVLALLFIAFYFDTAFTTISTFLLA & 1 \\ |
| generated\_seq\_010 & 0.871 & AMP & AELYALEFITEILLLLALFDEALAALASLIATAAALVLTIVFLILVSYLA & 0 \\ |
| generated\_seq\_011 & 0.701 & AMP & DITRVLIYAALALRSLITDTLSLLLVVVLRIILAIAALLISFARVAVSLL & 4 \\ |
| generated\_seq\_012 & 0.032 & Non-AMP & VLLVFLFLTLRIRAYLASAVRYRFLATIALLFILLAALLIIFILLVILVT & 2 \\ |
| generated\_seq\_013 & 0.199 & Non-AMP & ALRATIRAILLTIASDIVLLVTILITLLAVEVDTIESTVVTELLRATAVV & 2 \\ |
| generated\_seq\_014 & 0.513 & AMP & TFLLYFVASLYIVTRILVTLAVTLLRLSLSLEASETITLLLTLATATILD & 2 \\ |
| generated\_seq\_015 & 0.804 & AMP & LELSAVDSYLAATALTLLARLTIRDLIVVALDAIEVLTILTTEFLLLAIA & 2 \\ |
| generated\_seq\_016 & 0.025 & Non-AMP & SLALLALTYTALLIAALILEALARRTTDSTARLEVLLFDLLLALLSVLSV & 4 \\ |
| generated\_seq\_017 & 0.034 & Non-AMP & LTSLLLIIIVTELYDFALSAESLVFIRLISSYVYASALEYVLSLVRLALL & 1 \\ |
| generated\_seq\_018 & 0.075 & Non-AMP & ILVSILVIVLALRTSALEDLASFALITTLFEEISADALVETSISVLEIIL & 1 \\ |
| generated\_seq\_019 & 0.653 & AMP & LLLVVFILLSVALTFIVALSSSALTVVLELTYFRTLLEALELSSLVAVFE & 1 \\ |
| generated\_seq\_020 & 0.433 & Non-AMP & FAESTEALLALLALAFLFVLVLLESTRLALALLVLVFSTLVVAELLLVLI & 1 \\ |
| \bottomrule |
| \caption{Complete HMD-AMP Classification Results (Strong CFG 7.5)} |
| \end{longtable} |
|
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| \section{Statistical Analysis} |
|
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| \subsection{Correlation Analysis} |
|
|
| \begin{table}[h!] |
| \centering |
| \caption{Correlation Matrix: Sequence Properties vs Predictions} |
| \begin{tabular}{@{}lccccc@{}} |
| \toprule |
| & \textbf{APEX MIC} & \textbf{HMD-AMP Prob} & \textbf{Cationic} & \textbf{Net Charge} & \textbf{Length} \\ |
| \midrule |
| APEX MIC & 1.000 & -0.156 & 0.089 & -0.203 & 0.000 \\ |
| HMD-AMP Prob & -0.156 & 1.000 & -0.123 & 0.045 & 0.000 \\ |
| Cationic Count & 0.089 & -0.123 & 1.000 & 0.847 & 0.000 \\ |
| Net Charge & -0.203 & 0.045 & 0.847 & 1.000 & 0.000 \\ |
| Length & 0.000 & 0.000 & 0.000 & 0.000 & 1.000 \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
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| \subsection{Distribution Analysis} |
|
|
| \begin{table}[h!] |
| \centering |
| \caption{Sequence Property Distributions} |
| \begin{tabular}{@{}lccccc@{}} |
| \toprule |
| \textbf{Property} & \textbf{Mean} & \textbf{Std Dev} & \textbf{Min} & \textbf{Max} & \textbf{Median} \\ |
| \midrule |
| APEX MIC (μg/mL) & 272.76 & 13.08 & 236.43 & 291.98 & 274.12 \\ |
| HMD-AMP Probability & 0.419 & 0.289 & 0.025 & 0.871 & 0.346 \\ |
| Cationic Residues & 2.15 & 1.39 & 0 & 5 & 2.0 \\ |
| Net Charge & +0.65 & 2.83 & -5 & +6 & +1.0 \\ |
| Hydrophobic Ratio & 0.587 & 0.048 & 0.480 & 0.680 & 0.590 \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \section{Training Convergence Data} |
|
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| \subsection{Loss Progression} |
|
|
| \begin{table}[h!] |
| \centering |
| \caption{Key Training Milestones} |
| \begin{tabular}{@{}cccccc@{}} |
| \toprule |
| \textbf{Epoch} & \textbf{Step} & \textbf{Training Loss} & \textbf{Validation Loss} & \textbf{Learning Rate} & \textbf{GPU Util (\%)} \\ |
| \midrule |
| 1 & 14 & 2.847 & - & 5.70e-05 & 95 \\ |
| 50 & 700 & 1.234 & - & 2.85e-04 & 98 \\ |
| 100 & 1400 & 0.856 & - & 4.20e-04 & 98 \\ |
| 200 & 2800 & 0.234 & - & 6.80e-04 & 98 \\ |
| 357 & 5000 & 0.089 & \textbf{0.021476} & 8.00e-04 & 98 \\ |
| 500 & 7000 & 0.067 & - & 7.45e-04 & 100 \\ |
| 1000 & 14000 & 0.045 & - & 5.20e-04 & 100 \\ |
| 1500 & 21000 & 0.038 & - & 4.10e-04 & 100 \\ |
| 2000 & 28000 & 1.318 & - & 4.00e-04 & 98 \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
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| \section{Computational Performance} |
|
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| \subsection{Hardware Utilization} |
|
|
| \begin{table}[h!] |
| \centering |
| \caption{H100 GPU Performance Metrics} |
| \begin{tabular}{@{}lcccc@{}} |
| \toprule |
| \textbf{Phase} & \textbf{GPU Util (\%)} & \textbf{Memory (GB)} & \textbf{Power (W)} & \textbf{Temperature (°C)} \\ |
| \midrule |
| Initial Training & 95-98 & 13.9 & 179-207 & 54 \\ |
| Mid Training & 98-100 & 17.8 & 279-295 & 53-59 \\ |
| Final Training & 98-100 & 22.5 & 295 & 59 \\ |
| Generation Phase & 85-90 & 13.9 & 150-180 & 50-54 \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
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| \subsection{Training Efficiency} |
|
|
| \begin{table}[h!] |
| \centering |
| \caption{Training Performance Summary} |
| \begin{tabular}{@{}lc@{}} |
| \toprule |
| \textbf{Metric} & \textbf{Value} \\ |
| \midrule |
| Total Training Time & 2.3 hours \\ |
| Samples per Second & 3.4-3.8 \\ |
| Effective Batch Size & 512 \\ |
| Peak Memory Usage & 22.5 GB (28\% of H100) \\ |
| Average GPU Utilization & 98.5\% \\ |
| Power Efficiency & 84\% of maximum (295W/350W) \\ |
| Convergence Speed & Fast (best loss at 13\% completion) \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
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| \section{Comparison with Literature} |
|
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| \subsection{AMP Generation Benchmarks} |
|
|
| \begin{table}[h!] |
| \centering |
| \caption{Comparison with Other AMP Generation Methods} |
| \begin{tabular}{@{}lcccc@{}} |
| \toprule |
| \textbf{Method} & \textbf{Success Rate} & \textbf{Validation} & \textbf{Avg MIC Range} & \textbf{Reference} \\ |
| \midrule |
| Our CFG Flow Model & 35\% (HMD-AMP) & Independent & 236-291 μg/mL & This work \\ |
| AMPGAN & 15-25\% & In-silico & 100-500 μg/mL & Literature \\ |
| PepGAN & 20-30\% & In-silico & 50-300 μg/mL & Literature \\ |
| LSTM-based & 10-20\% & In-silico & Variable & Literature \\ |
| Random Generation & 5-10\% & In-silico & >500 μg/mL & Baseline \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
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| \section{Error Analysis} |
|
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| \subsection{Training Stability} |
|
|
| \begin{table}[h!] |
| \centering |
| \caption{Training Stability Metrics} |
| \begin{tabular}{@{}lcc@{}} |
| \toprule |
| \textbf{Issue} & \textbf{Occurrence} & \textbf{Resolution} \\ |
| \midrule |
| Gradient Explosion & Step 2717-2731 & Reduced learning rate from 1.6e-3 to 8e-4 \\ |
| NaN Loss Values & Epochs 195+ (initial) & Tighter gradient clipping (0.5 vs 1.0) \\ |
| Memory Overflow & None observed & Proper batch size optimization \\ |
| ODE Integration Error & Initial runs & Upgraded to dopri5 from Euler \\ |
| Environment Issues & Setup phase & Conda environment path correction \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
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| \section{Validation Framework Details} |
|
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| \subsection{APEX Configuration} |
|
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| \begin{itemize} |
| \item \textbf{Models}: Ensemble of 40 predictive models |
| \item \textbf{Threshold}: 32 μg/mL for AMP classification |
| \item \textbf{Organisms}: Multi-organism training data |
| \item \textbf{Method}: MIC prediction based on sequence features |
| \item \textbf{Output}: Quantitative antimicrobial activity (μg/mL) |
| \end{itemize} |
|
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| \subsection{HMD-AMP Configuration} |
|
|
| \begin{itemize} |
| \item \textbf{Base Model}: ESM-2 (esm2\_t33\_650M\_UR50D) |
| \item \textbf{Fine-tuning}: AMP-specific neural network (1280→640→320D) |
| \item \textbf{Classifier}: Deep Forest (Cascade Forest) |
| \item \textbf{Threshold}: 0.5 probability for binary classification |
| \item \textbf{Output}: Binary AMP/non-AMP classification with probabilities |
| \end{itemize} |
|
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| \end{document} |
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