scholarly journals Protein–Protein Interactions in Translesion Synthesis

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5544
Author(s):  
Radha Charan Dash ◽  
Kyle Hadden
Keyword(s):  
Protein Interactions ◽  
Acquired Resistance ◽  
Translesion Synthesis ◽  
Initial Response ◽  
Structural Features ◽  
Dna Lesions ◽  
Protein Protein Interactions ◽  
Dna Damage Tolerance ◽  
Response To Chemotherapy ◽  
Depth Analysis

Translesion synthesis (TLS) is an error-prone DNA damage tolerance mechanism used by actively replicating cells to copy past DNA lesions and extend the primer strand. TLS ensures that cells continue replication in the presence of damaged DNA bases, albeit at the expense of an increased mutation rate. Recent studies have demonstrated a clear role for TLS in rescuing cancer cells treated with first-line genotoxic agents by allowing them to replicate and survive in the presence of chemotherapy-induced DNA lesions. The importance of TLS in both the initial response to chemotherapy and the long-term development of acquired resistance has allowed it to emerge as an interesting target for small molecule drug discovery. Proper TLS function is a complicated process involving a heteroprotein complex that mediates multiple attachment and switching steps through several protein–protein interactions (PPIs). In this review, we briefly describe the importance of TLS in cancer and provide an in-depth analysis of key TLS PPIs, focusing on key structural features at the PPI interface while also exploring the potential druggability of each key PPI.

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.

In silico specificity determination of neoantigen-reactive T-lymphocytes

2021 ◽  
Vol 67 (3) ◽  
pp. 251-258
Author(s):  
A.E. Kniga ◽  
I.V. Polyakov ◽  
A.V. Nemukhin
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Molecular Docking ◽  
Protein Interactions ◽  
Structural Features ◽  
Immune Recognition ◽  
Protein Protein Interactions ◽  
Binary Classifiers

Effective personalized immunotherapies of the future will need to capture not only the peculiarities of the patient’s tumor but also of his immune response to it. In this study, using results of in vitro high-throughput specificity assays, and combining comparative models of pMHCs and TCRs using molecular docking, we have constructed all-atom models for the putative complexes of all their possible pairwise TCR-pMHC combinations. For the models obtained we have calculated a dataset of physics-based scores and have trained binary classifiers that perform better compared to their solely sequence-based counterparts. These structure-based classifiers pinpoint the most prominent energetic terms and structural features characterizing the type of protein-protein interactions that underlies the immune recognition of tumors by T cells.

scholarly journals Conserved Central Intraviral Protein Interactome of the Herpesviridae Family

mSystems ◽  
2019 ◽  
Vol 4 (5) ◽  
Author(s):  
Anna Hernández Durán ◽  
Kay Grünewald ◽  
Maya Topf
Keyword(s):  
Protein Interactions ◽  
Computational Models ◽  
Functional Organization ◽  
Driving Forces ◽  
Protein Complexes ◽  
Structural Features ◽  
System Level ◽  
Protein Protein Interactions ◽  
Protein Interactome ◽  
Essential Components

ABSTRACT Protein interactions are major driving forces behind the functional phenotypes of biological processes. As such, evolutionary footprints are reflected in system-level collections of protein-protein interactions (PPIs), i.e., protein interactomes. We conducted a comparative analysis of intraviral protein interactomes for representative species of each of the three subfamilies of herpesviruses (herpes simplex virus 1, human cytomegalovirus, and Epstein-Barr virus), which are highly prevalent etiologic agents of important human diseases. The intraviral interactomes were reconstructed by combining experimentally supported and computationally predicted protein-protein interactions. Using cross-species network comparison, we then identified family-wise conserved interactions and protein complexes, which we defined as a herpesviral “central” intraviral protein interactome. A large number of widely accepted conserved herpesviral protein complexes are present in this central intraviral interactome, encouragingly supporting the biological coherence of our results. Importantly, these protein complexes represent most, if not all, of the essential steps required during a productive life cycle. Hence the central intraviral protein interactome could plausibly represent a minimal infectious interactome of the herpesvirus family across a variety of hosts. Our data, which have been integrated into our herpesvirus interactomics database, HVint2.0, could assist in creating comprehensive system-level computational models of this viral lineage. IMPORTANCE Herpesviruses are an important socioeconomic burden for both humans and livestock. Throughout their long evolutionary history, individual herpesvirus species have developed remarkable host specificity, while collectively the Herpesviridae family has evolved to infect a large variety of eukaryotic hosts. The development of approaches to fight herpesvirus infections has been hampered by the complexity of herpesviruses’ genomes, proteomes, and structural features. The data and insights generated by our study add to the understanding of the functional organization of herpesvirus-encoded proteins, specifically of family-wise conserved features defining essential components required for a productive infectious cycle across different hosts, which can contribute toward the conceptualization of antiherpetic infection strategies with an effect on a broader range of target species. All of the generated data have been made freely available through our HVint2.0 database, a dedicated resource of curated herpesvirus interactomics purposely created to promote and assist future studies in the field.

scholarly journals Signaling Protein Networks as Targets of New Antineoplastic Drugs

2003 ◽  
Vol 18 (1) ◽  
pp. 57-61 ◽  
Author(s):  
S. Alberti ◽  
S. Parodi
Keyword(s):  
Protein Interactions ◽  
High Throughput Screening ◽  
Regulatory Networks ◽  
Signaling Proteins ◽  
Protein Protein Interactions ◽  
Therapeutic Approaches ◽  
Chemical Libraries ◽  
Drug Candidates ◽  
Microbial Extracts ◽  
Depth Analysis

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from “-omic” approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, “intelligent” drug candidates. An additional medicinal chemistry strategy (via 40–50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.

scholarly journals Mutation effect estimation on protein–protein interactions using deep contextualized representation learning

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Guangyu Zhou ◽  
Muhao Chen ◽  
Chelsea J T Ju ◽  
Zheng Wang ◽  
Jyun-Yu Jiang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Pair ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Point Mutations ◽  
Representation Learning ◽  
Structural Features ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Subtle Change

Abstract The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein–protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein–protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR.

Understanding Protein–Protein Interactions Using Local Structural Features

2013 ◽  
Vol 425 (7) ◽  
pp. 1210-1224 ◽  
Author(s):  
Joan Planas-Iglesias ◽  
Jaume Bonet ◽  
Javier García-García ◽  
Manuel A. Marín-López ◽  
Elisenda Feliu ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Structural Features ◽  
Protein Protein Interactions

scholarly journals Novel Role for the C Terminus of Saccharomyces cerevisiae Rev1 in Mediating Protein-Protein Interactions

2006 ◽  
Vol 26 (21) ◽  
pp. 8173-8182 ◽  
Author(s):  
Sanjay D'Souza ◽  
Graham C. Walker
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Dna Damage ◽  
Protein Interactions ◽  
Sequence Similarity ◽  
Dna Lesions ◽  
Dominant Negative ◽  
Dominant Negative Effect ◽  
Protein Protein Interactions ◽  
C Terminus ◽  
Negative Effect

ABSTRACT The Saccharomyces cerevisiae REV3/7-encoded polymerase ζ and Rev1 are central to the replicative bypass of DNA lesions, a process called translesion synthesis (TLS). While yeast polymerase ζ extends from distorted DNA structures, Rev1 predominantly incorporates C residues from across a template G and a variety of DNA lesions. Intriguingly, Rev1 catalytic activity does not appear to be required for TLS. Instead, yeast Rev1 is thought to participate in TLS by facilitating protein-protein interactions via an N-terminal BRCT motif. In addition, higher eukaryotic homologs of Rev1 possess a C terminus that interacts with other TLS polymerases. Due to a lack of sequence similarity, the yeast Rev1 C-terminal region, located after the polymerase domain, had initially been thought not to play a role in TLS. Here, we report that elevated levels of the yeast Rev1 C terminus confer a strong dominant-negative effect on viability and induced mutagenesis after DNA damage, highlighting the crucial role that the C terminus plays in DNA damage tolerance. We show that this phenotype requires REV7 and, using immunoprecipitations from crude extracts, demonstrate that, in addition to the polymerase-associated domain, the extreme Rev1 C terminus and the BRCT region of Rev1 mediate interactions with Rev7.

scholarly journals Contacts-based prediction of binding affinity in protein–protein complexes

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Anna Vangone ◽  
Alexandre MJJ Bonvin
Keyword(s):  
Binding Affinity ◽  
Conformational Changes ◽  
Protein Interactions ◽  
Prediction Accuracy ◽  
Protein Complexes ◽  
Structural Features ◽  
Specific Protein ◽  
Strong Impact ◽  
Protein Protein Interactions ◽  
Almost All

Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

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