ProAffiMuSeq: sequence-based method to predict the binding free energy change of protein–protein complexes upon mutation using functional classification

2019 ◽  
Author(s):  
Sherlyn Jemimah ◽  
Masakazu Sekijima ◽  
M Michael Gromiha
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Large Scale ◽  
Structural Information ◽  
Protein Complexes ◽  
Binding Free Energy ◽  
Complex Structure ◽  
External Validation ◽  
Functional Class ◽  
Supplementary Information

Abstract Motivation Protein–protein interactions are essential for the cell and mediate various functions. However, mutations can disrupt these interactions and may cause diseases. Currently available computational methods require a complex structure as input for predicting the change in binding affinity. Further, they have not included the functional class information for the protein–protein complex. To address this, we have developed a method, ProAffiMuSeq, which predicts the change in binding free energy using sequence-based features and functional class. Results Our method shows an average correlation between predicted and experimentally determined ΔΔG of 0.73 and mean absolute error (MAE) of 0.86 kcal/mol in 10-fold cross-validation and correlation of 0.75 with MAE of 0.94 kcal/mol in the test dataset. ProAffiMuSeq was also tested on an external validation set and showed results comparable to structure-based methods. Our method can be used for large-scale analysis of disease-causing mutations in protein–protein complexes without structural information. Availability and implementation Users can access the method at https://web.iitm.ac.in/bioinfo2/proaffimuseq/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein–protein binding affinity

2020 ◽  
Author(s):  
Gen Li ◽  
Swagata Pahari ◽  
Adithya Krishna Murthy ◽  
Siqi Liang ◽  
Robert Fragoza ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Binding ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Structural Information ◽  
Binding Free Energy ◽  
Supplementary Information ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Genome Scale

Abstract Motivation Vast majority of human genetic disorders are associated with mutations that affect protein–protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein–protein binding free energy to assist the development of therapeutic solutions. Currently, the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available. Results Here, we report a Gradient Boosting Decision Tree machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further, a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37–0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein–protein interactions. Availability and implementation SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. Supplementary information Supplementary data are available at Bioinformatics online.

pyDockEneRes: per-residue decomposition of protein–protein docking energy

2019 ◽  
Vol 36 (7) ◽  
pp. 2284-2285 ◽  
Author(s):  
Miguel Romero-Durana ◽  
Brian Jiménez-García ◽  
Juan Fernández-Recio
Keyword(s):  
Binding Affinity ◽  
Protein Interactions ◽  
Structural Model ◽  
Protein Complexes ◽  
Complex Structure ◽  
Protein Docking ◽  
Supplementary Information ◽  
Scoring Functions ◽  
Residue Decomposition ◽  
Docking Energy

Abstract Motivation Protein–protein interactions are key to understand biological processes at the molecular level. As a complement to experimental characterization of protein interactions, computational docking methods have become useful tools for the structural and energetics modeling of protein–protein complexes. A key aspect of such algorithms is the use of scoring functions to evaluate the generated docking poses and try to identify the best models. When the scoring functions are based on energetic considerations, they can help not only to provide a reliable structural model for the complex, but also to describe energetic aspects of the interaction. This is the case of the scoring function used in pyDock, a combination of electrostatics, desolvation and van der Waals energy terms. Its correlation with experimental binding affinity values of protein–protein complexes was explored in the past, but the per-residue decomposition of the docking energy was never systematically analyzed. Results Here, we present pyDockEneRes (pyDock Energy per-Residue), a web server that provides pyDock docking energy partitioned at the residue level, giving a much more detailed description of the docking energy landscape. Additionally, pyDockEneRes computes the contribution to the docking energy of the side-chain atoms. This fast approach can be applied to characterize a complex structure in order to identify energetically relevant residues (hot-spots) and estimate binding affinity changes upon mutation to alanine. Availability and implementation The server does not require registration by the user and is freely accessible for academics at https://life.bsc.es/pid/pydockeneres. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Mapping, Structure and Modulation of PPI

2021 ◽  
Vol 9 ◽  
Author(s):  
Elisa Martino ◽  
Sara Chiarugi ◽  
Francesco Margheriti ◽  
Gianpiero Garau
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Hot Spots ◽  
Molecular Level ◽  
Protein Complexes ◽  
Binding Free Energy ◽  
Detailed Knowledge ◽  
Protein Protein Interactions ◽  
Advanced Technologies ◽  
Successful Design

Because of the key relevance of protein–protein interactions (PPI) in diseases, the modulation of protein-protein complexes is of relevant clinical significance. The successful design of binding compounds modulating PPI requires a detailed knowledge of the involved protein-protein system at molecular level, and investigation of the structural motifs that drive the association of the proteins at the recognition interface. These elements represent hot spots of the protein binding free energy, define the complex lifetime and possible modulation strategies. Here, we review the advanced technologies used to map the PPI involved in human diseases, to investigate the structure-function features of protein complexes, and to discover effective ligands that modulate the PPI for therapeutic intervention.

scholarly journals The Protein’s Role in Substrate Positioning and Reactivity for Biosynthetic Enzyme Complexes: the Case of SyrB2/SyrB1

2018 ◽  
Author(s):  
Rimsha Mehmood ◽  
Helena W. Qi ◽  
Adam H. Steeves ◽  
Heather Kulik
Keyword(s):  
Free Energy ◽  
Large Scale ◽  
Structural Information ◽  
Acyl Carrier Protein ◽  
Protein Complexes ◽  
Heme Iron ◽  
Free Energy Barrier ◽  
Chemical Transformations ◽  
Biosynthetic Enzyme ◽  
Enzyme Complexes

Biosynthetic enzyme complexes selectively catalyze challenging chemical transformations, including alkane functionalization (e.g., halogenation of threonine, Thr, by non-heme iron SyrB2). However, the role of complex formation in enabling reactivity and guiding selectivity is poorly understood, owing to the challenges associated with obtaining detailed structural information of the dynamically associating protein complexes. Combining over 10 ms of classical molecular dynamics of SyrB2 and the acyl carrier protein SyrB1 with large-scale QM/MM simulation, we investigate the substrate–protein and protein–protein dynamics that give rise to experimentally observed substrate positioning and reactivity trends. We confirm the presence of a hypothesized substrate-delivery channel in SyrB2 through free energy simulations that show channel opening with a low free energy barrier. We identify stabilizing interactions at the SyrB2/SyrB1 interface that are compatible with phosphopantatheine (PPant) delivery of substrate to SyrB2. By sampling metal–substrate distances observed in experimental spectroscopy of native SyrB2/SyrB1-PPant-<i>S</i>-Thr and non-native substrates, we characterize essential protein–substrate interactions that are responsible for substrate positioning, and thus, reactivity. We observe the hydroxyl sidechain and terminal amine of the native Thr substrate to form cooperative hydrogen bonds with a single N123 residue in SyrB2. In comparison, non-native substrates that lack the hydroxyl interact more flexibly with the protein and therefore can orient closer to the Fe center, explaining their preferential hydroxylation and higher turnover frequencies.

scholarly journals Topology independent structural matching discovers novel templates for protein interfaces

2017 ◽  
Author(s):  
Claudio Mirabello ◽  
Björn Wallner
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Complexes ◽  
Structural Alignment ◽  
Complex Structure ◽  
Potential Interaction ◽  
Supplementary Information ◽  
Alignment Algorithms ◽  
Protein Interfaces ◽  
Wide Range

AbstractMotivationProtein-protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein-protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. While there are many structural alignment algorithms for individual proteins, very few have been developed for protein interfaces, and none that can align only the interface residues to other interfaces or surfaces of interacting monomer subunits in a topology independent (non-sequential) manner.ResultsWe present InterComp, a method for topology and sequence-order independent structural comparisons. The method is general and can be applied to various structural comparison applications. By representing residues as independent points in space rather than as a sequence of residues, InterComp can can be applied to a wide range of problems including: interface-surface comparisons, interface-interface comparisons and even comparisons of small molecule ligands. We demonstrate a use-case by applying InterComp to find similar protein interfaces on the surface of proteins. We show that InterComp pinpoints the correct interface for almost half of the targets (283 of 586) when considering the top 10 hits, and for 24% of the top 1, even when no templates can be found with the already available sequence-order dependent methods like TM-align.AvailabilityThe program is available from: http://wallnerlab.org/[email protected] informationSupplementary data included in the pdf.

scholarly journals The Protein’s Role in Substrate Positioning and Reactivity for Biosynthetic Enzyme Complexes: the Case of SyrB2/SyrB1

2018 ◽  
Author(s):  
Rimsha Mehmood ◽  
Helena W. Qi ◽  
Adam H. Steeves ◽  
Heather Kulik
Keyword(s):  
Free Energy ◽  
Large Scale ◽  
Structural Information ◽  
Acyl Carrier Protein ◽  
Protein Complexes ◽  
Heme Iron ◽  
Free Energy Barrier ◽  
Chemical Transformations ◽  
Biosynthetic Enzyme ◽  
Enzyme Complexes

Biosynthetic enzyme complexes selectively catalyze challenging chemical transformations, including alkane functionalization (e.g., halogenation of threonine, Thr, by non-heme iron SyrB2). However, the role of complex formation in enabling reactivity and guiding selectivity is poorly understood, owing to the challenges associated with obtaining detailed structural information of the dynamically associating protein complexes. Combining over 10 ms of classical molecular dynamics of SyrB2 and the acyl carrier protein SyrB1 with large-scale QM/MM simulation, we investigate the substrate–protein and protein–protein dynamics that give rise to experimentally observed substrate positioning and reactivity trends. We confirm the presence of a hypothesized substrate-delivery channel in SyrB2 through free energy simulations that show channel opening with a low free energy barrier. We identify stabilizing interactions at the SyrB2/SyrB1 interface that are compatible with phosphopantatheine (PPant) delivery of substrate to SyrB2. By sampling metal–substrate distances observed in experimental spectroscopy of native SyrB2/SyrB1-PPant-<i>S</i>-Thr and non-native substrates, we characterize essential protein–substrate interactions that are responsible for substrate positioning, and thus, reactivity. We observe the hydroxyl sidechain and terminal amine of the native Thr substrate to form cooperative hydrogen bonds with a single N123 residue in SyrB2. In comparison, non-native substrates that lack the hydroxyl interact more flexibly with the protein and therefore can orient closer to the Fe center, explaining their preferential hydroxylation and higher turnover frequencies.

scholarly journals Reliable identification of protein-protein interactions by crosslinking mass spectrometry

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Swantje Lenz ◽  
Ludwig R. Sinn ◽  
Francis J. O’Reilly ◽  
Lutz Fischer ◽  
Fritz Wegner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Structural Information ◽  
Protein Complexes ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
False Discovery Rates ◽  
Large Scale Analysis ◽  
Exit Tunnel

AbstractProtein-protein interactions govern most cellular pathways and processes, and multiple technologies have emerged to systematically map them. Assessing the error of interaction networks has been a challenge. Crosslinking mass spectrometry is currently widening its scope from structural analyses of purified multi-protein complexes towards systems-wide analyses of protein-protein interactions (PPIs). Using a carefully controlled large-scale analysis of Escherichia coli cell lysate, we demonstrate that false-discovery rates (FDR) for PPIs identified by crosslinking mass spectrometry can be reliably estimated. We present an interaction network comprising 590 PPIs at 1% decoy-based PPI-FDR. The structural information included in this network localises the binding site of the hitherto uncharacterised protein YacL to near the DNA exit tunnel on the RNA polymerase.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

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