Hot Spots In Protein Research Articles

Enhancing cisplatin drug sensitivity through PARP3 inhibition: The influence on PDGF and G-coupled signal pathways in cancer

Drug resistance poses a significant challenge in cancer treatment despite the clinical efficacy of cisplatin. Identifying and targeting biomarkers open new ways to improve therapeutic outcomes. In this study, comprehensive bioinformatic analyses were employed, including a comparative analysis of multiple datasets, to evaluate overall survival and mutation hotspots in 27 base excision repair (BER) genes of more than 7,500 tumors across 23 cancer types.By using various parameters influencing patient survival, revealing that the overexpression of 15 distinct BER genes, particularly PARP3, NEIL3, and TDG, consistently correlated with poorer survival across multiple factors such as race, gender, and metastasis. Single nucleotide polymorphism (SNP) analyses within protein-coding regions highlighted the potential deleterious effects of mutations on protein structure and function. The investigation of mutation hotspots in BER proteins identified PARP3 due to its high mutation frequency.Moving from bioinformatics to wet lab experiments, cytotoxic experiments demonstrated that the absence of PARP3 by CRISPR/Cas9-mediated knockdown in MDA-MB-231 breast cancer cells increased drug activity towards cisplatin, carboplatin, and doxorubicin. Pathway analyses indicated the impact of PARP3 absence on the platelet-derived growth factor (PDGF) and G-coupled signal pathways on cisplatin exposure.PDGF, a critical regulator of various cellular functions, was downregulated in the absence of PARP3, suggesting a role in cancer progression. Moreover, the influence of PARP3 knockdown on G protein-coupled receptors (GPCRs) affects their function in the presence of cisplatin.In conclusion, the study demonstrated a synthetic lethal interaction between GPCRs, PDGF signaling pathways, and PARP3 gene silencing. PARP3 emerged as a promising target.

The Interplay between Protein Frustration and Hotspot Formation

Hotspots situated at protein-protein interfaces, allosteric and orthosteric binding sites, traditionally play a crucial role in the initial phases of drug discovery, as they are useful to identify druggable targets and guide the optimization of fragments into lead compounds. Despite the high level of protein frustration, which can be thought of as the percentage of residues that cannot independently achieve minimum energy due to tridimensional restraints, observed at those locations, limited efforts have been made to investigate any potential connection between the localized frustration and hotspots in proteins. This review paper aims to partially address this knowledge gap by proposing that the origin of hotspots is, at least in part, due to localized frustration in proteins. While there is no hard evidence to support this hypothesis, the results obtained by integrating in silico tools predicting hot spot locations, with those calculating localized protein frustration, suggest that druggable hotspots are surrounded by residues involved in frustrated contacts. Additionally, the inclusion of long-range electrostatic terms in the protein frustration calculation enables the identification of an equal to a higher number of frustrated residues. These observations indirectly suggest that localized protein frustration around druggable hotspots could guide the development of more potent leads.

FPGA Implementation of IIR Notch and Anti-Notch Filters With an Application to Localization of Protein Hot-Spots.

In this paper, high-speed second-order infinite impulse response (IIR) notch filter (NF) and anti-notch filter (ANF) are designed and realized on hardware. The improvement in speed of operation for the NF is then achieved by using the re-timing concept. The ANF is designed to specify a stability margin and minimize the amplitude area. Next, an improved approach is proposed for the detection of protein hot-spot locations using the designed second-order IIR ANF. The analytical and experimental results reported in this paper show that the proposed approach provides better hot-spot prediction compared to the reported classical filtering techniques based on the IIR Chebyshev filter and S-transform. The proposed approach also yields consistency in prediction hot-spots compared to the results based on biological methodologies. Furthermore, the presented technique reveals some new "potential" hot-spots. The proposed filters are simulated and synthesized using the Xilinx Vivado 18.3 software platform with Zynq-7000 Series (ZedBoard Zynq Evaluation and Development Kit xc7z020clg484-1) FPGA family.

Research trends and hotspots of glial fibrillary acidic protein within the area of Alzheimer's disease: a bibliometric analysis.

Our aim was to analyze the trends and hotspots on glial fibrillary acidic protein (GFAP) within the area of Alzheimer's disease (AD) by using a bibliometric method, which is currently missing. All articles and reviews on GFAP within the area of AD from inception to December 31, 2022, were searched from the Web of Science Core Collection. Full records were derived, imported into Microsoft Excel, and analyzed by BIBLIOMETRC, VOSviewer, and CiteSpace. In total, 2,269 publications, including 2,166 articles, were ultimately included. The number of publications from 81 countries/regions and 527 academic journals increased annually. The top three prolific countries and institutions were the USA, China, and England, the University of Gothenburg (Sweden), Universidade Federal Rio Grande do Sul (Brasilia), and UCL Queen Square Institute of Neurology (England). Henrik Zetterberg from the University of Gothenburg, Kaj Blennow from the University of Gothenburg, and Alexei Verkhratsky from the University of Manchester were the top three prolific and cited authors; Journal of Alzheimer's Disease, Brain Research, and Neuroscience contributed the most publications. The top key areas of research included "molecular, biology, and genetics" and "molecular, biology, and immunology," and the top published and linked meaningful keywords included oxidative stress, inflammation/neuroinflammation, microglia, hippocampus, amyloid, cognitive impairment, tau, and dysfunction. Based on the bibliometric analysis, the number of publications on GFAP within the area of AD has been rapidly increasing, especially in the past several years. Oxidative stress and inflammation are research hotspots, and GFAP in body fluids, especially blood, could be used for large-scale screening for AD.

Evaluating EIIP and CPNR Methods for Protein Hotspot Prediction

Evaluating EIIP and CPNR Methods for Protein Hotspot Prediction

Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform

BackgroundIdentification of hot spots in protein–DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein–DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein–DNA features to predict hot spots, unable to make full use of the effective information in the features.ResultsIn this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model.ConclusionsOur method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH.

Benjamin F. Cravatt III – Chemical Proteomics Trailblazer

AbstractThe advances in large‐scale bioanalytical methods and computational approaches have afforded tremendous insight into fundamental mechanisms of health and disease as well as provided new opportunities for therapeutic development. However, the majority of proteins in humans remain undrugged or lack chemical probes to explore their functions in vivo. The pioneering studies on activity‐based protein profiling (ABPP) by Ben Cravatt and his lab members have provided a systematic and generalizable approach to address this major challenge in chemical biology and drug discovery. Notably, the Cravatt laboratory paved the way for employing activity‐ and reactivity‐based chemical probes with quantitative proteomics to identify small molecule protein targets and discover unpredicted protein hot spots for native and synthetic ligands. Importantly, these chemical proteomic methods have revealed new protein targets and mechanisms of action for drug discovery and have been widely adopted by many other laboratories around the world to discover new biology and pharmacology. The recognition of Ben Cravatt as a co‐recipient of the 2022 Wolf Prize in Chemistry for this trailblazing work in chemical proteomics is most well‐deserved.

In silico engineering a CD80 variant with increased affinity to CTLA-4 and decreased affinity to CD28 for optimized cancer immunotherapy

CD80 or cluster of differentiation 80, also known as B7–1, is a member of the immunoglobulin super family, which binds to CTLA-4 and CD28 T cell receptors and induces inhibitory and inductive signals respectively. Although CTLA-4 and CD28 receptors belong to the same protein family, slight differences in their structures leads to CD80 having a higher binding affinity to CTLA-4 (−14.55 kcal/mol) compared with CD28(−12.51 kcal/mol). In this study, we constructed a variant of CD80 protein with increased binding affinity to CTLA-4 and decreased binding affinity to CD28. This variant has no signaling capability, and can act as a cap for these receptors to protect them from natural CD80 proteins existing in the body. The first step was the evolutionary and alanine scanning analysis of CD80 protein to determine conserved regions in this protein. Next, complex alanine scanning technique was employed to determine CD80 protein hotspots in CD80-CTLA-4 and CD80-CD28 protein complexes. This information was fed into a computational model developed in R for in silico mutagenesis and CD80 variant library construction. The 3D structures of variants were modeled using the Swiss model webserver. After modeling the 3D structures, HADDOCK server was employed to build all protein-protein complexes, which contain CTLA-4-CD80 variant complexes, Wild type CD80-CD28 complexes and CD28-CD80 variant complexes. Protein-protein binding free energy was determined using FoldX and the variant number 316 with mutations at 29, 31, 33 positions showed increased binding affinity to CTLA-4 (−21.43 kcal/mol) and decreased binding affinity to CD28 (− 9.54 kcal/mol). Finally, molecular dynamics (MD) simulations confirmed the stability of variant 316. In conclusion, we designed a new CD80 protein variant with potential immunotherapeutic applications.

Fragment screening libraries for the identification of protein hot spots and their minimal binding pharmacophores.

Fragment-based drug design relies heavily on structural information for the elaboration and optimisation of hits. The ability to identify neighbouring binding hot spots, energetically favourable interactions and conserved binding motifs in protein structures through X-ray crystallography can inform the evolution of fragments into lead-like compounds through structure-based design. The composition of fragment libraries can be designed and curated to fit this purpose and herein, we describe and compare screening libraries containing compounds comprising between 2 and 18 heavy atoms. We evaluate the properties of the compounds in these libraries and assess their ability to probe protein surfaces for binding hot spots.

Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.

Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. The process is cumbersome, time-consuming and laborious to some extent. This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. In order to obtain large data samples, this work integrates and extracts data from the datasets of ASEdb, BID, SKEMPI and dbMPIKT to generate a new dataset, and adopts the SMOTE algorithm to expand positive samples to form the training set. The experimental results show that the method achieves an F1 score of 0.82 on the test set. Compared with other hot spot prediction methods, our model achieved better prediction performance.

In Silico Prediction Method for Protein Asparagine Deamidation.

In silico prediction methods were developed to predict protein asparagine (Asn) deamidation. The method is based on understanding deamidation mechanism on structural level with machine learning. Our structure-based method is more accurate than the sequence-based method which is still widely used in protein engineering process. In addition, molecular dynamics simulation was applied to study the time occupancy of nucleophilic attack distance, which is hypothesized as the most important step toward the rate-limiting succinimide intermediate formation. A more accurate prediction method for distinguishing potentially liable amino acid residues would allow their elimination or reduction as early as possible in the drug discovery process. It is possible that such quantitative protein structure-property relationship tools can also be applied to other protein hotspot predictions.

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins.

A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.

Elucidation of protein function using computational docking and hotspot analysis by ClusPro and FTMap.

Starting with a crystal structure of a macromolecule, computational structural modeling can help to understand the associated biological processes, structure and function, as well as to reduce the number of further experiments required to characterize a given molecular entity. In the past decade, two classes of powerful automated tools for investigating the binding properties of proteins have been developed: the protein-protein docking program ClusPro and the FTMap and FTSite programs for protein hotspot identification. These methods have been widely used by the research community by means of publicly available online servers, and models built using these automated tools have been reported in a large number of publications. Importantly, additional experimental information can be leveraged to further improve the predictive power of these approaches. Here, an overview of the methods and their biological applications is provided together with a brief interpretation of the results.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation.

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integratethese data sources will play increasingly important roles in disease gene discovery and variant interpretation.

Identification of Novel GSK-3β Hits Using Competitive Biophysical Assays

Glycogen synthase kinase 3 beta (GSK-3β) is an evolutionarily conserved serine-threonine kinase dysregulated in numerous pathologies, such as Alzheimer’s disease and cancer. Even though GSK-3β is a validated pharmacological target most of its inhibitors have two main limitations: the lack of selectivity due to the high homology that characterizes the ATP binding site of most kinases, and the toxicity that emerges from GSK-3β complete inhibition which translates into the impairment of the plethora of pathways GSK-3β is involved in. Starting from a 1D 19F NMR fragment screening, we set up several biophysical assays for the identification of GSK-3β inhibitors capable of binding protein hotspots other than the ATP binding pocket or to the ATP binding pocket, but with an affinity able of competing with a reference binder. A phosphorylation activity assay on a panel of several kinases provided selectivity data that were further rationalized and corroborated by structural information on GSK-3β in complex with the hit compounds. In this study, we identified promising fragments, inhibitors of GSK-3β, while proposing an alternative screening workflow that allows facing the flaws that characterize the most common GSK-3β inhibitors through the identification of selective inhibitors and/or inhibitors able to modulate GSK-3β activity without leading to its complete inhibition.

A two-step ensemble learning for predicting protein hot spot residues from whole protein sequence.

Protein hot spot residues are functional sites in protein-protein interactions. Biological experimental methods are traditionally used to identify hot spot residues, which is laborious and time-consuming. Thus a variety of computational methods were widely used in recent years. Despite the success of computational methods in hot spot identification, most of them are impractical in reality because they can recognize hot spot residues only from known protein-protein interface residues. Therefore, identifying hot spots from whole protein sequence is a meaningful and interesting issue. However, it will bring extreme imbalance between positive and negative samples. Hot spot residues only account for about 1-2% of whole protein sequences. To address the issue, this paper proposes a two-step ensemble model for identifying hot spot residues from extremely unbalanced data set. The model is composed of 134 classifiers constructed by base KNN and SVM. Compared to the previous methods, our model yields good performance with an F1 score of 0.593 on the BID test set. Furthermore, to validate the robustness of our model, it was tested on other three independent test sets and also achieved good predictions. More importantly, the performance of our model tested on unbalanced data set is comparable with other methods tested on balanced hot spot data set.

NMR Spectroscopy of the Main Protease of SARS‐CoV‐2 and Fragment‐Based Screening Identify Three Protein Hotspots and an Antiviral Fragment

AbstractThe main protease (3CLp) of the SARS‐CoV‐2, the causative agent for the COVID‐19 pandemic, is one of the main targets for drug development. To be active, 3CLp relies on a complex interplay between dimerization, active site flexibility, and allosteric regulation. The deciphering of these mechanisms is a crucial step to enable the search for inhibitors. In this context, using NMR spectroscopy, we studied the conformation of dimeric 3CLp from the SARS‐CoV‐2 and monitored ligand binding, based on NMR signal assignments. We performed a fragment‐based screening that led to the identification of 38 fragment hits. Their binding sites showed three hotspots on 3CLp, two in the substrate binding pocket and one at the dimer interface. F01 is a non‐covalent inhibitor of the 3CLp and has antiviral activity in SARS‐CoV‐2 infected cells. This study sheds light on the complex structure‐function relationships of 3CLp and constitutes a strong basis to assist in developing potent 3CLp inhibitors.

NMR Spectroscopy of the Main Protease of SARS-CoV-2 and Fragment-Based Screening Identify Three Protein Hotspots and an Antiviral Fragment.

The main protease (3CLp) of the SARS‐CoV‐2, the causative agent for the COVID‐19 pandemic, is one of the main targets for drug development. To be active, 3CLp relies on a complex interplay between dimerization, active site flexibility, and allosteric regulation. The deciphering of these mechanisms is a crucial step to enable the search for inhibitors. In this context, using NMR spectroscopy, we studied the conformation of dimeric 3CLp from the SARS‐CoV‐2 and monitored ligand binding, based on NMR signal assignments. We performed a fragment‐based screening that led to the identification of 38 fragment hits. Their binding sites showed three hotspots on 3CLp, two in the substrate binding pocket and one at the dimer interface. F01 is a non‐covalent inhibitor of the 3CLp and has antiviral activity in SARS‐CoV‐2 infected cells. This study sheds light on the complex structure‐function relationships of 3CLp and constitutes a strong basis to assist in developing potent 3CLp inhibitors.

Local topology and bifurcation hot-spots in proteins with SARS-CoV-2 spike protein as an example.

Novel topological methods are introduced to protein research. The aim is to identify hot-spot sites where a bifurcation can alter the local topology of the protein backbone. Since the shape of a protein is intimately related to its biological function, a substitution that causes a bifurcation should have an enhanced capacity to change the protein’s function. The methodology applies to any protein but it is developed with the SARS-CoV-2 spike protein as a timely example. First, topological criteria are introduced to identify and classify potential bifurcation hot-spot sites along the protein backbone. Then, the expected outcome of asubstitution, if it occurs, is estimated for a general class of hot-spots, using a comparative analysis of the surrounding backbone segments. The analysis combines the statistics of structurally commensurate amino acid fragments in the Protein Data Bank with general stereochemical considerations. It is observed that the notorious D614G substitution of the spike protein is a good example of a bifurcation hot-spot. A number of topologically similar examples are then analyzed in detail, some of them are even better candidates for a bifurcation hot-spot than D614G. The local topology of the more recently observed N501Y substitution is also inspected, and it is found that this site is proximal to a different kind of local topology changing bifurcation.

VirusMED: an atlas of hotspots of viral proteins.

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.