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1. BioModME for building and simulating dynamic computational models of complex biological systems

   https://doi.org/10.1093/bioadv/vbae023  

   Womack, Justin A. ; Shah, Viren ; Audi, Said H. ; Terhune, Scott S. ; Dash, Ranjan K. ; Lengauer, ed., Thomas ( February 2024 , Bioinformatics Advances)

   Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed “BioModME,” a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization.

   All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

    

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2. SSIPe: accurately estimating protein–protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function

   https://doi.org/10.1093/bioinformatics/btz926  

   Huang, Xiaoqiang ; Zheng, Wei ; Pearce, Robin ; Zhang, Yang ; Valencia, ed., Alfonso ( December 2019 , Bioinformatics)

   Most proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein–protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses.

   We present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities.

   Web-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe.

   Supplementary data are available at Bioinformatics online.

    

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3. Predicting binding affinity changes from long‐distance mutations using molecular dynamics simulations and Rosetta

   https://doi.org/10.1002/prot.26477  

   Wells, Nicholas G. M. ; Smith, Colin A. ( March 2023 , Proteins: Structure, Function, and Bioinformatics)

   Computationally modeling how mutations affect protein–protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high‐throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long‐distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high‐throughput Rosetta‐based side‐chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long‐distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long‐distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein–protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.

    

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4. PIQLE: protein–protein interface quality estimation by deep graph learning of multimeric interaction geometries

   https://doi.org/10.1093/bioadv/vbad070  

   Shuvo, Md Hossain ; Karim, Mohimenul ; Roche, Rahmatullah ; Bhattacharya, Debswapna ; Gromiha, ed., Michael ( June 2023 , Bioinformatics Advances)

   Accurate modeling of protein–protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations.

   Here, we present PIQLE, a deep graph learning method for protein–protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE.

   An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE.

   Supplementary data are available at Bioinformatics Advances online.

    

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5. VSCode-Antimony: a source editor for building, analyzing, and translating antimony models

   https://doi.org/10.1093/bioinformatics/btad753  

   Ma, Steve ; Fan, Longxuan ; Konanki, Sai Anish ; Liu, Eva ; Gennari, John H. ; Smith, Lucian P. ; Hellerstein, Joseph L. ; Sauro, Herbert M. ; Martelli, ed., Pier Luigi ( December 2023 , Bioinformatics)

   Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not “model aware,” they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features.

   We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML).

   VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

    

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