Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.

This dataset is associated with the MuToN project hosted on GitHub (https://github.com/zpliulab/MuToN). It includes mutation records, PPI complexes, and pre-computed LLM embeddings for the SKEMPI dataset.

Contents:

├── data/
│  ├── skempi_v2.csv        # contains the mutation records in SKEMPI dataset.
│  ├── SKEMPI/
│  │  ├── raws/          # contains the PPI complexes in SKEMPI dataset.
│  │  │  ├── 1CSE.pdb
│  │  ├── raw_pdb/         # contains the single wild and mutant structure.
│  │  │  ├── 1CSE_E.pdb      # extrated from 1CSE.pdb
│  │  │  ├── 1CSE_I.pdb
│  │  │  ├── 1CSE_I.mut.38_E.pdb # computed mutant structure of 1CSE_E.pdb
│  │  ├── llm_embedding/      # contains the pre-computed LLM embeddings using ESM-2.
│  │  │  ├── 1CSE_E.npy      # LLM embedding of 1CSE_E.pdb, shape=(L, 1280)
│  │  │  ├── 1CSE_I.npy
│  │  │  ├── 1CSE_I.mut.38_E.npy

Mutation Stability Prediction

  Overview

The Mutation Stability Prediction (MSP) task involves classifying whether mutations in the SKEMPI 2.0 database (J. Jankauskaite, B. Jiménez-García et al., 2019) are stabilizing or not using the provided protein structures. Each mutation in the MSP task includes a PDB file with the residue of interest transformed to the specified mutant amino acid as well as the native PDB file. A total of 4148 mutant structures accompanied by their… See the full description on the dataset page: https://huggingface.co/datasets/vector-institute/atom3d-msp.

(A) Shows PCC between experimental ΔΔG with the respective Δlog10(koff) and Δlog10(kon) for single-point alanine, single-point non-alanine, multi-point and all 713 mutations. (B) Shows PCC between Int_HS_Energy with the respective ΔΔG, Δlog10(koff) and Δlog10(kon) for single-point alanine, single-point non-alanine, multi-point and all 713 mutations. Experimental values for the 713 mutations used here are extracted from SKEMPI [41] and are presented in Dataset S1.

Pearson's Correlation Coefficient (PCC) of hotspot descriptors with experimental Δlog10(koff) for the 713 off-rate mutations in SKEMPI.

Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. The process is cumbersome, time-consuming and laborious to some extent. This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. In order to obtain large data samples, this work integrates and extracts data from the datasets of ASEdb, BID, SKEMPI and dbMPIKT to generate a new dataset, and adopts the SMOTE algorithm to expand positive samples to form the training set. The experimental results show that the method achieves an F1 score of 0.82 on the test set. Compared with other hot spot prediction methods, our model achieved better prediction performance.