A computational tool to predict the evolutionarily conserved protein–protein interaction hot‐spot residues from the structure of the unbound protein

Agrawal, Neeraj J.; Helk, Bernhard; Trout, Bernhardt L.

doi:10.1016/j.febslet.2013.11.004

Cited by 27 publications

(16 citation statements)

References 44 publications

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“…The interface is formed by spatially neighboring residues whose ASA between single domain and complex were changed more than 1 Å 2 per site and cross-interface contacts distance < 5 Å. The other surface residues are non-interface [14, 26, 27]. The accessible surface area (ASA) of residues was computed using NACCESS (http://www.bioinf.manchester.ac.uk/naccess/).…”

Section: Methodsmentioning

confidence: 99%

A novel index of protein-protein interface propensity improves interface residue recognition

Dai

et al. 2016

BMC Syst Biol

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BackgroundProtein-protein interface holds important information of protein-protein interactions which play key roles in most biological processes. In the past few years, a lot of efforts have been made to improve interface residue recognition by characterizing protein-protein interfaces and extracting relevant features. However, most previous studies were carried out in a qualitative level, and there are also some inconsistencies between them.ResultsIn the present work, to improve interface residue recognition, we built a novel quantitative residue protein-protein interface propensity index (QIPI) and gained a comprehensive picture of protein-protein interface through analyzing protein-protein interfaces on our comprehensive protein-protein interfaces dataset (Astral2.05-40-4506). Furthermore, in order to assess the effect of QIPI in improving the protein-protein interface prediction, we developed an interface residue recognition method SPR (Single domain based Patch Recognition) based on the QIPI. The evaluation results proved that our novel QIPI is able to improve the interface residue recognition.ConclusionsThrough a comprehensive quantitative analysis of protein-protein interface, we constructed a novel quantitative protein-protein interface propensity index (QIPI), which could be easily applied to improve the interface residue recognition and helpful in understanding the protein-protein interface.AvailabilityQIPI and SPR are available to non-commercial users at our website: http://www.scbit.org/QIPI/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0351-7) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

A novel index of protein-protein interface propensity improves interface residue recognition

Dai

et al. 2016

BMC Syst Biol

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“…Structures of the Fab domain of mAb01 and other antibodies were generated and 20 ns of molecular dynamics (MD) simulations in explicit water were performed as outlined in Lauer et al (). For each antibody, from the last 10 ns of MD simulation trajectory, a structure of the Fab domain was extracted at every 0.1 ns and the spatial interaction map (SIM) tool (Agrawal et al, ) was applied to these 100 structures to identify clusters of exposed hydrophobic residues on the Fab domain surface. For the SIM tool, a hydrophobic cutoff value of Ф cutoff = 0.15 was used and the sequence conservation criterion was not used.…”

Section: Methodsmentioning

confidence: 99%

Fast and scalable purification of a therapeutic full‐length antibody based on process crystallization

Smejkal

Agrawal

Helk

et al. 2013

Biotech & Bioengineering

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The potential of process crystallization for purification of a therapeutic monoclonal IgG1 antibody was studied. The purified antibody was crystallized in non-agitated micro-batch experiments for the first time. A direct crystallization from clarified CHO cell culture harvest was inhibited by high salt concentrations. The salt concentration of the harvest was reduced by a simple pretreatment step. The crystallization process from pretreated harvest was successfully transferred to stirred tanks and scaled-up from the mL-scale to the 1 L-scale for the first time. The crystallization yield after 24 h was 88-90%. A high purity of 98.5% was reached after a single recrystallization step. A 17-fold host cell protein reduction was achieved and DNA content was reduced below the detection limit. High biological activity of the therapeutic antibody was maintained during the crystallization, dissolving, and recrystallization steps. Crystallization was also performed with impure solutions from intermediate steps of a standard monoclonal antibody purification process. It was shown that process crystallization has a strong potential to replace Protein A chromatography. Fast dissolution of the crystals was possible. Furthermore, it was shown that crystallization can be used as a concentrating step and can replace several ultra-/diafiltration steps. Molecular modeling suggested that a negative electrostatic region with interspersed exposed hydrophobic residues on the Fv domain of this antibody is responsible for the high crystallization propensity. As a result, process crystallization, following the identification of highly crystallizable antibodies using molecular modeling tools, can be recognized as an efficient, scalable, fast, and inexpensive alternative to key steps of a standard purification process for therapeutic antibodies.

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“…The method computes the propensity of a given residue to be located at the interface in the 100 lowest-energy rigid body docking solutions, and can reach high precision in the prediction of hot-spots, but at the expense of low sensitivity. Another method that do not need the complex structure is SIM (Agrawal, Helk, & Trout, 2014), which predicts hot-spot residues involved in evolutionarily conserved protein-protein interactions.…”

Section: Interface and Hot-spot Predictionmentioning

confidence: 99%

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

Barradas-Bautista

Rosell

Pallara

et al. 2018

Advances in Protein Chemistry and Structural Biology

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A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein-protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein-protein interactions, or providing modeled structural data for drug discovery targeting protein-protein interactions.

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A computational tool to predict the evolutionarily conserved protein–protein interaction hot‐spot residues from the structure of the unbound protein

Cited by 27 publications

References 44 publications

A novel index of protein-protein interface propensity improves interface residue recognition

A novel index of protein-protein interface propensity improves interface residue recognition

Fast and scalable purification of a therapeutic full‐length antibody based on process crystallization

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

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