<scp>ppdx</scp>: Automated modeling of protein–protein interaction descriptors for use with machine learning

Conti, Simone; Ovchinnikov, Victor; Karplus, Martin

doi:10.1002/jcc.26974

Cited by 6 publications

(2 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each structure was refined by CHARMM [ 42 ], using energy minimization. The model generation and the evaluation of the scoring functions were performed via the python program ppdx [ 49 ], which is freely available online at (accessed on 28 July 2022), and includes the scripts with parameters used to create the homology models for this study. We tested 18 scoring functions found in the literature, as described below.…”

Section: Methodsmentioning

confidence: 99%

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

2022

Self Cite

View full text Add to dashboard Cite

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

show abstract

Section: Methodsmentioning

confidence: 99%

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Alternative approaches using machine learning (ML) to predict or classify binding partners at a lower computational cost have emerged in the past decade and are mostly directed at protein–ligand complexes due to the interest of the pharmaceutical industry in the drug design field. − Only a few applications have been dedicated to predicting the binding of protein–protein complexes. ,− The methodologies employed to address this challenge can be broadly categorized into two distinct categories, leveraging either sequence or structural characteristics . In the case of sequence-based approaches, features rely on evolutionarily conserved residues, hypothesized to be essential for the function and, therefore, for binding .…”

Section: Introductionmentioning

confidence: 99%

Classifying Protein–Protein Binding Affinity with Free-Energy Calculations and Machine Learning Approaches

Goulard Coderc de Lacam,

Roux,

Chipot

2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Understanding the intricate phenomenon of neuronal wiring in the brain is of great interest in neuroscience. In the fruit fly, Drosophila melanogaster, the Dpr-DIP interactome has been identified to play an important role in this process. However, experimental data suggest that a merely limited subset of complexes, essentially 57 out of a total of 231, exhibit strong binding affinity. In this work, we sought to identify the residue-level molecular basis underlying the difference in binding affinity using a state-of-the-art methodology consisting of standard binding free-energy calculations with a geometrical route and machine learning (ML) techniques. We determined the binding affinity for two complexes using statistical mechanics simulations, achieving an excellent reproduction of the experimental data. Moreover, we predicted the binding free energy for two additional lowaffinity complexes, devoid of experimental estimation, while simultaneously identifying key residues for the binding. Furthermore, through the use of ML algorithms, linear discriminant analysis, and random forest, we achieved remarkable accuracy, as high as 0.99, in discerning between strong (cognate) and weak (noncognate) binders. The presented ML approach encompasses easily transferable input features, enabling its broad application to any interactome while facilitating the identification of pivotal residues critical for binding interactions. The predictive power of the generated model was probed on similar protein families from 13 diverse species. Our ML model exhibited commendable performance on these additional data sets, showcasing its reliability and robustness across the species barrier.

show abstract

High‐throughput molecular simulations of SARS‐CoV‐2 receptor binding domain mutants quantify correlations between dynamic fluctuations and protein expression

Ovchinnikov,

Karplus

2024

J Comput Chem

View full text Add to dashboard Cite

Prediction of protein fitness from computational modeling is an area of active research in rational protein design. Here, we investigated whether protein fluctuations computed from molecular dynamics simulations can be used to predict the expression levels of SARS‐CoV‐2 receptor binding domain (RBD) mutants determined in the deep mutational scanning experiment of Starr et al. [Science (New York, N.Y.) 2022, 377, 420] Specifically, we performed more than 0.7 milliseconds of molecular dynamics (MD) simulations of 557 mutant RBDs in triplicate to achieve statistical significance under various simulation conditions. Our results show modest but significant anticorrelation in the range [−0.4, −0.3] between expression and RBD protein flexibility. A simple linear regression machine learning model achieved correlation coefficients in the range [0.7, 0.8], thus outperforming MD‐based models, but required about 25 mutations at each residue position for training.

show abstract

ppdx: Automated modeling of protein–protein interaction descriptors for use with machine learning

Cited by 6 publications

References 66 publications

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

Classifying Protein–Protein Binding Affinity with Free-Energy Calculations and Machine Learning Approaches

High‐throughput molecular simulations of SARS‐CoV‐2 receptor binding domain mutants quantify correlations between dynamic fluctuations and protein expression

Contact Info

Product

Resources

About