# KinForm: Kinetics Informed Feature Optimised Representation Models for Enzyme $k\_{cat}$ and $K\_{M}$ Prediction

Source: [KinForm: Kinetics Informed Feature Optimised Representation Models for Enzyme $k\_{cat}$ and $K\_{M}$ Prediction](<https://www.sota2.com/research/paper/68fa5f7c1e209131ce9ea3ac>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:18:45.922Z

Authors: Saleh Alwer, Ronan Fleming

Published: 2025-07-19T14:34:57.000Z

## Abstract

Kinetic parameters such as the turnover number ($k\_{cat}$) and Michaelis constant ($K\_{\\mathrm{M}}$) are essential for modelling enzymatic activity but experimental data remains limited in scale and diversity. Previous methods for predicting enzyme kinetics typically use mean-pooled residue embeddings from a single protein language model to represent the protein. We present KinForm, a machine learning framework designed to improve predictive accuracy and generalisation for kinetic parameters by optimising protein feature representations. KinForm combines several residue-level embeddings (Evolutionary Scale Modeling Cambrian, Evolutionary Scale Modeling 2, and ProtT5-XL-UniRef50), taken from empirically selected intermediate transformer layers and applies weighted pooling based on per-residue binding-site probability. To counter the resulting high dimensionality, we apply dimensionality reduction using principal--component analysis (PCA) on concatenated protein features, and rebalance the training data via a similarity-based oversampling strategy. KinForm outperforms baseline methods on two benchmark datasets. Improvements are most pronounced in low sequence similarity bins. We observe improvements from binding-site probability pooling, intermediate-layer selection, PCA, and oversampling of low-identity proteins. We also find that removing sequence overlap between folds provides a more realistic evaluation of generalisation and should be the standard over random splitting when benchmarking kinetic prediction models.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2507.14639>)

[PDF](<https://arxiv.org/pdf/2507.14639.pdf>)