Linking structural features of protein complexes and biological function

Sowmya, Gopichandran; Breen, Edmond J.; Ranganathan, Shoba

doi:10.1002/pro.2736

Cited by 37 publications

(33 citation statements)

References 54 publications

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“…H-bonds increases with interface size among these complexes except for non-obligatory complexes (Table 1 and Figure 1c & 1k) and (Figure 4 c, g, k) with an average influence of 15% ± 6.5%. Moreover, electrostatic energy increases with interface size among non-obligatory regulator inhibitors (Table 1 and Figure 5h) and its role is significant among this group as reported elsewhere [23]. It is interesting to note the percentage of electrostatic energy is almost non-existent on average in enzymes and enzyme inhibitors (Table 3).…”

Section: Figuresupporting

confidence: 72%

“…(14). Simultaneously class B (113) is grouped into obligatory (76), immune (14) and non-obligatory (23). The obligatory protein complexes are further classified into enzyme (11), regulator (56) and biological assembly (9) and the nonobligatory protein complexes into enzyme inhibitor (10) and regulator inhibitor (13).…”

Section: Resultsmentioning

confidence: 99%

“…Interfaces with less non-polar residues compared to surface [21,22] in addition to interfaces with more non-polar residues than surface are intriguing [8]. Description of interface area, hydrogen bonds, solvation free energy gain and binding energy to distinguish functional classes is impressive [23]. These observations have largely improved our understanding of the interfaces using 3 interfaces [6] in 1975 to 278 interfaces [23] in 2015.…”

Section: Introductionmentioning

confidence: 99%

“…Description of interface area, hydrogen bonds, solvation free energy gain and binding energy to distinguish functional classes is impressive [23]. These observations have largely improved our understanding of the interfaces using 3 interfaces [6] in 1975 to 278 interfaces [23] in 2015. Conclusions drawn thus far are dependent on dataset size (number of complexes), type (homo, hetero, mixed) and analysis methods (residue or atomic models).…”

Section: Introductionmentioning

confidence: 99%

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2017

Bioinformation

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Several catalysis, cellular regulation, immune function, cell wall assembly, transport, signaling and inhibition occur through Protein-Protein Interactions (PPI). This is possible with the formation of specific yet stable protein-protein interfaces. Therefore, it is of interest to understand its molecular principles using structural data in relation to known function. Several interface features have been documented using known X-ray structures of protein complexes since 1975. This has improved our understanding of the interface using structural features such as interface area, binding energy, hydrophobicity, relative hydrophobicity, salt bridges and hydrogen bonds. The strength of binding between two proteins is dependent on interface size (number of residues at the interface) and thus its corresponding interface area. It is known that large interfaces have high binding energy (sum of (van der Waals) vdW, H-bonds, electrostatics). However, the selective role played by each of these energy components and more especially that of vdW is not explicitly known. Therefore, it is important to document their individual role in known protein-protein structural complexes. It is of interest to relate interface size with vdW, H-bonds and electrostatic interactions at the interfaces of protein structural complexes with known function using statistical and multiple linear regression analysis methods to identify the prominent force. We used the manually curated non-redundant dataset of 278 hetero-dimeric protein structural complexes grouped using known functions by Sowmya et al. (2015) to gain additional insight to this phenomenon using a robust inter-atomic non-covalent interaction analyzing tool PPCheck (Anshul and Sowdhamini, 2015). This dataset consists of obligatory (enzymes, regulator, biological assembly), immune and nonobligatory (enzyme and regulator inhibitors) complexes. Results show that the total binding energy is more for large interfaces. However, this is not true for its individual energy factors. Analysis shows that vdW energies contribute to about 75% ±11% on average among all complexes and it also increases with interface size (r 2 ranging from 0.67 to 0.89 with p<0.01) at 95% confidence limit irrespective of molecular function. Thus, vdW is both dominant and proportional at the interface independent of molecular function. Nevertheless, H bond energy contributes to 15% ± 6.5% on average in these complexes. It also moderately increases with interface size (r 2 ranging from 0.43 to 0.61 with p<0.01) only among obligatory and immune complexes. Moreover, there is about 11.3% ± 8.7% contribution by electrostatic energy. It increases with interface size specifically among non-obligatory regulator-inhibitors (r 2 = 0.44). It is implied that both H-bonds and electrostatics are neither dominant nor proportional at the interface. Nonetheless, their presence cannot be ignored in binding. Therefore, H-bonds and (or) electrostatic energy having specific role for improved stability in complexes is implied. Thus, vdW is comm...

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Section: Figuresupporting

confidence: 72%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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2017

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“…Van der Waals forces, ionic interactions, hydrogen bonds, interactions with water molecules, and hydrophobic effects form the physicochemical foundations of PPIs (Keskin et al, 2008). Taking a more macromolecular view, the combination of interface shape, its amino acid composition, hydrogen bonds, the solvation free energy gain from interface formation, and the eventual binding energy all play important roles in PPIs (Sowmya et al, 2015). Single changes at the amino acid level often do not influence an interaction substantially due to the large surface areas that contribute to most interaction sites (Reichmann et al, 2007;Aakre et al, 2015).…”

Section: Screening Approachesmentioning

confidence: 99%

Techniques for the analysis of protein-protein interactions in vivo

et al. 2016

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ORCID ID: 0000-0002-5820-4466 (C.G.).Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of crossspecies networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique.

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Calculation of hot spots for protein–protein interaction in p53/PMI‐MDM2/MDMX complexes

Huang

Song

et al. 2018

J Comput Chem

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The recently developed MM/GBSA_IE method is applied to computing hot and warm spots in p53/PMI-MDM2/MDMX proteinprotein interaction systems. Comparison of the calculated hot (>2 kcal/mol) and warm spots (>1 kcal/mol) in P53 and PMI proteins interacting with MDM2 and MDMX shows a good quantitative agreement with the available experimental data. Further, our calculation predicted hot spots in MDM2 and MDMX proteins in their interactions with P53 and PMI and they help elucidate the interaction mechanism underlying this important PPI system. In agreement with the experimental result, the present calculation shows that PMI has more hot and warm spots and binds stronger to MDM2/MDMX. The analysis of these hot and warm spots helps elucidate the fundamental difference in binding between P53 and PMI to the MDM2/MDMX systems. Specifically, for p53/PMI-MDM2 systems, p53 and PMI use essentially the same residues (L54, I61, Y67, Q72, V93, H96, and I99) of MDM2 for binding. However, PMI enhanced interactions with residues L54, Y67, and Q72 of MDM2. For the p53/PMI-MDMX system, p53 and PMI use similar residues (M53, I60, Y66, Q71, V92, and Y99) of MDMX for binding. However, PMI exploited three extra residues (M61, K93, and L98) of MDMX for enhanced binding. In addition, PMI enhanced interaction with four residues (M53, Y66, Q71, and Y99) of MDMX. These results gave quantitative explanation on why the binding affinities of PMI-MDM2/MDMX interactions are stronger than that of p53-MDM2/MDMX although their binding modes are similar.

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Linking structural features of protein complexes and biological function

Cited by 37 publications

References 54 publications

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Techniques for the analysis of protein-protein interactions in vivo

Calculation of hot spots for protein–protein interaction in p53/PMI‐MDM2/MDMX complexes

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