Interface Residues Research Articles

Underlying features for the enhanced electrostatic strength of the extremophilic malate dehydrogenase interface salt-bridge compared to the mesophilic one

Malate dehydrogenase (MDH) exists in multimeric form in normal and extreme solvent conditions where residues of the interface are involved in specific interactions. The interface salt-bridge (ISB) and its microenvironment (ME) residues may have a crucial role in the stability and specificity of the interface. To gain insight into this, we have analyzed 218 ISBs from 42 interfaces of 15 crystal structures along with their sequences. Comparative analyses demonstrate that the ISB strength is ∼30 times greater in extremophilic cases than that of the normal one. To this end, the interface residue propensity, ISB design and pair selection, and ME-residue’s types, i.e., type-I and type-II, are seen to be intrinsically involved. Although Type-I is a common type, Type-II appears to be extremophile-specific, where the net ME-residue count is much lower with an excessive net ME-energy contribution, which seems to be a novel interface compaction strategy. Furthermore, the interface strength can be enhanced by selecting the desired mutant from the net-energy profile of all possible mutations of an unfavorable ME-residue. The study that applies to other similar systems finds applications in protein–protein interaction and protein engineering. Communicated by Ramaswamy H. Sarma

Experimental modification in thermal stability of oligomers by alanine substitution and site saturation mutagenesis of interfacial residues

For certain industrial applications, the stability of protein oligomers is important. In this study, we demonstrated an efficient method to improve the thermal stability of oligomers using the trimeric protein chloramphenicol acetyltransferase (CAT) as the model. We substituted all interfacial residues of CAT with alanine to detect residues critical for oligomer stability. Mutation of six of the forty-nine interfacial residues enhanced oligomer thermal stability. Site saturation mutagenesis was performed on these six residues to optimize the side chains. About 15% of mutations enhanced thermal stability by more than 0.5 °C and most did not disrupt activity of CAT. Certain combinations of mutations further improved thermal stability and resistance against heat treatment. The quadruple mutant, H17V/N34S/F134A/D157C, retained the same activity as the wild-type after heat treatment at 9 °C higher temperature than the wild-type CAT. Furthermore, combinations with only alanine substitutions also improved thermal stability, suggesting the method we developed can be used for rapid modification of industrially important proteins.

Interfacial residues in protein-protein complexes are in the eyes of the beholder.

Interactions between proteins are vital in almost all biological processes. The characterization of protein-protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein-protein complexes are identified in a complementary manner.

DeepUMQA3: a web server for accurate assessment of interface residue accuracy in protein complexes.

Model quality assessment is a crucial part of protein structure prediction and a gateway to proper usage of models in biomedical applications. Many methods have been proposed for assessing the quality of structural models of protein monomers, but few methods for evaluating protein complex models. As protein complex structure prediction becomes a new challenge, there is an urgent need for model quality assessment methods that can accurately assess the accuracy of interface residues of complex structures. Here, we present DeepUMQA3, a web server for evaluating the accuracy of interface residues of protein complex structures using deep neural networks. For an input complex structure, features are extracted from three levels of overall complex, intra-monomer, and inter-monomer, and an improved deep residual neural network is used to predict per-residue lDDT and interface residue accuracy. DeepUMQA3 ranks first in the blind test of interface residue accuracy estimation in CASP15, with Pearson, Spearman, and AUC of 0.564, 0.535, and 0.755 under the lDDT measurement, which are 17.6%, 23.6%, and 10.9% higher than the second best method, respectively. DeepUMQA3 can also assess the accuracy of all residues in the entire complex and distinguish high- and low-precision residues. The web sever of DeepUMQA3 are freely available at http://zhanglab-bioinf.com/DeepUMQA_server/.

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots.

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. In this chapter, we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction.The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 (angiotensin converting enzyme 2)receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

Design principles for cyclin K molecular glue degraders

Molecular glue degraders are an effective therapeutic modality, but their design principles are not well understood. Recently, several unexpectedly diverse compounds were reported to deplete cyclin K by linking CDK12–cyclin K to the DDB1–CUL4–RBX1 E3 ligase. Here, to investigate how chemically dissimilar small molecules trigger cyclin K degradation, we evaluated 91 candidate degraders in structural, biophysical and cellular studies and reveal all compounds acquire glue activity via simultaneous CDK12 binding and engagement of DDB1 interfacial residues, in particular Arg928. While we identify multiple published kinase inhibitors as cryptic degraders, we also show that these glues do not require pronounced inhibitory properties for activity and that the relative degree of CDK12 inhibition versus cyclin K degradation is tuneable. We further demonstrate cyclin K degraders have transcriptional signatures distinct from CDK12 inhibitors, thereby offering unique therapeutic opportunities. The systematic structure–activity relationship analysis presented herein provides a conceptual framework for rational molecular glue design.

Low Ohmic contact resistance and high on/off ratio in transition metal dichalcogenides field-effect transistors via residue-free transfer.

Beyond-silicon technology demands ultrahigh performance field-effect transistors. Transition metal dichalcogenides provide an ideal material platform, but the device performances such as the contact resistance, on/off ratio and mobility are often limited by the presence of interfacial residues caused by transfer procedures. Here, we show an ideal residue-free transfer approach using polypropylene carbonate with a negligible residue coverage of ~0.08% for monolayer MoS2 at the centimetre scale. By incorporating a bismuth semimetal contact with an atomically clean monolayer MoS2 field-effect transistor on hexagonal boron nitride substrate, we obtain an ultralow Ohmic contact resistance of ~78 Ω µm, approaching the quantum limit, and a record-high on/off ratio of ~1011 at 15 K. Such an ultra-clean fabrication approach could be the ideal platform for high-performance electrical devices using large-area semiconducting transition metal dichalcogenides.

Categorization of hotspots into three types – weak, moderate and strong to distinguish protein–protein versus protein–peptide interactions

Protein–protein and protein–peptide interactions (PPI and PPepI) belong to a similar category of interactions, yet seemingly subtle differences exist among them. To characterize differences between protein–protein (PP) and protein–peptide (PPep) interactions, we have focussed on two important classes of residues-hotspot and anchor residues. Using implicit solvation-based free energy calculations, a very large-scale alanine scanning has been performed on benchmark datasets, consisting of over 5700 interface residues. The differences in the two categories are more pronounced, if the data were divided into three distinct types, namely – weak hotspots (having binding free energy loss upon Ala mutation, ΔΔG, ∼2–10 kcal/mol), moderate hotspots (ΔΔG, ∼10–20 kcal/mol) and strong hotspots (ΔΔG ≥ ∼20 kcal/mol). The analysis suggests that for PPI, weak hotspots are predominantly populated by polar and hydrophobic residues. The distribution shifts towards charged and polar residues for moderate hotspot and charged residues (principally Arg) are overwhelmingly present in the strong hotspot. On the other hand, in the PPepI dataset, the distribution shifts from predominantly hydrophobic and polar (in the weak type) to almost similar preference for polar, hydrophobic and charged residues (in moderate type) and finally the charged residue (Arg) and Trp are mostly occupied in the strong type. The preferred anchor residues in both categories are Arg, Tyr and Leu, possessing bulky side chain and which also strike a delicate balance between side chain flexibility and rigidity. The present knowledge should aid in effective design of biologics, by augmentation or disruption of PPIs with peptides or peptidomimetics. Communicated by Ramaswamy H. Sarma

Estimating protein complex model accuracy based on ultrafast shape recognition and deep learning in CASP15.

This article reports and analyzes the results of protein complex model accuracy estimation by our methods (DeepUMQA3 and GraphGPSM) in the 15th Critical Assessment of techniques for protein Structure Prediction (CASP15). The new deep learning-based multimeric complex model accuracy estimation methods are proposed based on the ensemble of three-level features coupling with deep residual/graph neural networks. For the input multimeric complex model, we describe it from three levels: overall complex features, intra-monomer features, and inter-monomer features. We designed an overall ultrafast shape recognition (USR) to characterize the relationship between local residues and the overall complex topology, and an inter-monomer USR to characterize the relationship between the residues of one monomer and the topology of other monomers. DeepUMQA3 (Group name: GuijunLab-RocketX) ranked first in the interface residue accuracy estimation of CASP15. The Pearson correlation between the interface residue Local Distance Difference Test (lDDT) predicted by DeepUMQA3 and the real lDDT is 0.570, the only method that exceeds 0.5. Among the top 5 methods, DeepUMQA3 achieved the highest Pearson correlation of lDDT on 25 out of 39 targets. GraphGPSM (Group name: GuijunLab-PAthreader) has TM-score Pearson correlations greater than 0.9 on 14 targets, showing a good ability to estimate the overall fold accuracy. The DeepUMQA3 server is available at http://zhanglab-bioinf.com/DeepUMQA/ and the GraphGPSM server is available at http://zhanglab-bioinf.com/GraphGPSM/.

Probing the origins of programmed death ligand-1 inhibition by implementing machine learning-assisted sequential virtual screening techniques.

PD-L1 is a key immunotarget involved in binding to its receptor PD-1. PD-L1/PD-1 interface blocking using antibodies (or small molecules) is the central area of interest for tumor suppression in various cancers. Blocking the PD-L1/PD-1 pathway in the tumor cells results in its immune activation and destruction, and thereby restoring the T-cell proliferation and cytokine production. The active binding site interface residues of PD-L1/PD-1 were experimentally known and proven by structural biology and site-directed mutagenesis studies. Structure-based molecular design technique was employed to identify the inhibitors for blocking the PD-L1/PD-1 interface. Nine hits to leads were identified from the SPECS small molecule database by machine learning, molecular docking, and molecular dynamics simulation techniques. Following this, a machine learning-assisted QSAR modeling approach was implemented using ChEMBL database to gain insights into the inhibitory potential of PD-L1 inhibitors and predict the activity of our previously screened nine hit molecules. The best leads identified in the present study bind strongly with the active sites of PD-L1/PD-1 interface residues, which include A121, M115, I116, S117, I54, Y56, D122, and Y123. These computational leads are considered promising molecules for furtherin vitroandin vivoanalysis to be developed as potential PD-L1 checkpoint inhibitors to cure different types of cancers.

GPCRome-wide analysis of G-protein-coupling diversity using a computational biology approach

GPCRs are master regulators of cell signaling by transducing extracellular stimuli into the cell via selective coupling to intracellular G-proteins. Here we present a computational analysis of the structural determinants of G-protein-coupling repertoire of experimental and predicted 3D GPCR-G-protein complexes. Interface contact analysis recapitulates structural hallmarks associated with G-protein-coupling specificity, including TM5, TM6 and ICLs. We employ interface contacts as fingerprints to cluster Gs vs Gi complexes in an unsupervised fashion, suggesting that interface residues contribute to selective coupling. We experimentally confirm on a promiscuous receptor (CCKAR) that mutations of some of these specificity-determining positions bias the coupling selectivity. Interestingly, Gs-GPCR complexes have more conserved interfaces, while Gi/o proteins adopt a wider number of alternative docking poses, as assessed via structural alignments of representative 3D complexes. Binding energy calculations demonstrate that distinct structural properties of the complexes are associated to higher stability of Gs than Gi/o complexes. AlphaFold2 predictions of experimental binary complexes confirm several of these structural features and allow us to augment the structural coverage of poorly characterized complexes such as G12/13.

Uncovering interactions of mitochondrial proteins BNIP3 and BNIP3L with necroptosis-associated kinase RIPK3: Insights into kinase activation

Mitochondria, an important organelle implicated in programmed cell death, assumes a crucial role in necroptosis. However, the regulatory mechanisms through which mitochondria participates in necroptosis are largely unknown. To address this knowledge gap, our study aimed to identify mitochondrial proteins that engage in interactions with receptor-interacting protein kinase 3 (RIPK3), a significant upstream kinase involved in necroptosis. Among the candidates, BNIP3 and BNIP3L exhibited significant higher binding scores to RIPK3 compared to others. Computational modeling revealed specific interactions, as RIPK3 specifically binds to a conserved α-helix region within BNIP3 and BNIP3L. Validation experiments confirmed the significance of these helical peptides for RIPK3 binding. Conserved peptides were also identified in BNIP3 and BNIP3L proteins from various animal species, including humans. The binding between human RIPK3 and BNIP3/BNIP3L peptides demonstrated perfect shape and charge complementation, with highly conserved interface residues. Moreover, peptide binding stabilized an active conformation of RIPK3, potentially enhancing its kinase activity. These findings uncover the interactions between RIPK3 and BNIP3/BNIP3L, providing insights into RIPK3 regulation and its role in necroptosis.

Exploring the Binding Mechanism of NRG1–ERBB3 Complex and Discovery of Potent Natural Products to Reduce Diabetes-Assisted Breast Cancer Progression

Diabetes mellitus significantly contributes to breast cancer progression, where hyperglycemia upregulates specific genes, leading to more aggressive tumor growth. In patients with BC that develop diabetes, neuregulin 1 (NRG1) and epidermal growth factor receptor 3 (ERBB3) overexpression exacerbate tumor growth and progression. Since the interaction between NRG1 and ERBB3 is critical for tumor growth, understanding the molecular mechanisms underlying NRG1–ERBB3 complex formation is essential for elucidating diabetes-assisted breast cancer progression. However, the key residues forming the NRG1–ERBB3 complex remain unknown. Here, we substituted specific residues in NRG1 with alanine and studied its interactions with ERBB3 using computational structural biology tools. We further screened the South African natural compounds database to target the complex’s interface residues to discover potential inhibitors. The conformational stability and dynamic features of NRG1–WT, –H2A, –L3A, and –K35A complexed with ERBB3 were subjected to 400 ns molecular dynamics simulations. The free binding energies of all NRG1–ERBB3 complexes were calculated using the molecular mechanics-generalized Born surface area (MM/GBSA). The H2 and L3 alanine substitutions caused a loss of interaction with ERBB3 residue D73, weakening the interaction with ERBB3. Screening 1300 natural compounds identified four (SANC00643, SANC00824, SANC00975, and SANC00335) with the best potential to inhibit ERRB3-NRG1 coupling. The binding free energies for each complex were − 48.55 kcal/mol for SANC00643, − 47.68 kcal/mol for SANC00824, − 46.04 kcal/mol for SANC00975, and − 45.29 kcal/mol for SANC00335, showing their overall stronger binding with ERBB3 than NRG1 and their potential to act as ERBB3-NRG1 complex inhibitors. In conclusion, this complex may represent a residue-specific drug target to inhibit BC progression.Graphical abstract

Differences in the organization of interface residues tunes the stability of the SARS-CoV-2 spike-ACE2 complex.

The continuous emergence of novel variants represents one of the major problems in dealing with the SARS-CoV-2 virus. Indeed, also due to its prolonged circulation, more than ten variants of concern emerged, each time rapidly overgrowing the current viral version due to improved spreading features. As, up to now, all variants carry at least one mutation on the spike Receptor Binding Domain, the stability of the binding between the SARS-CoV-2 spike protein and the human ACE2 receptor seems one of the molecular determinants behind the viral spreading potential. In this framework, a better understanding of the interplay between spike mutations and complex stability can help to assess the impact of novel variants. Here, we characterize the peculiarities of the most representative variants of concern in terms of the molecular interactions taking place between the residues of the spike RBD and those of the ACE2 receptor. To do so, we performed molecular dynamics simulations of the RBD-ACE2 complexes of the seven variants of concern in comparison with a large set of complexes with different single mutations taking place on the RBD solvent-exposed residues and for which the experimental binding affinity was available. Analyzing the strength and spatial organization of the intermolecular interactions of the binding region residues, we found that (i) mutations producing an increase of the complex stability mainly rely on instaurating more favorable van der Waals optimization at the cost of Coulombic ones. In particular, (ii) an anti-correlation is observed between the shape and electrostatic complementarities of the binding regions. Finally, (iii) we showed that combining a set of dynamical descriptors is possible to estimate the outcome of point mutations on the complex binding region with a performance of 0.7. Overall, our results introduce a set of dynamical observables that can be rapidly evaluated to probe the effects of novel isolated variants or different molecular systems.

In Silico Identification of Natural Product-Based Inhibitors Targeting IL-1β/IL-1R Protein–Protein Interface

IL-1β mediates inflammation and regulates immune responses, cell proliferation, and differentiation. Dysregulation of IL-1β is linked to inflammatory and autoimmune diseases. Elevated IL-1β levels are found in patients with severe COVID-19, indicating its excessive production may worsen the disease. Also, dry eye disease patients show high IL-1β levels in tears and conjunctival epithelium. Therefore, IL-1β signaling is a potential therapeutic targeting for COVID-19 and aforementioned diseases. No small-molecule IL-1β inhibitor is clinically approved despite efforts. Developing such inhibitors is highly desirable. Herein, a docking-based strategy was used to screen the TCM (Traditional Chinese Medicine) database to identify possible IL-1β inhibitors with desirable pharmacological characteristics by targeting the IL-1β/IL-1R interface. Primarily, the docking-based screening was performed by selecting the crucial residues of IL-1β interface to retrieve the potential compounds. Afterwards, the compounds were shortlisted on the basis of binding scores and significant interactions with the crucial residues of IL-1β. Further, to gain insights into the dynamic behavior of the protein–ligand interactions, MD simulations were performed. The analysis suggests that four selected compounds were stabilized in an IL-1β pocket, possibly blocking the formation of an IL-1β/IL-1R complex. This indicates their potential to interfere with the immune response, making them potential therapeutic agents to investigate further.

Investigation into the Interaction Sites of the K84s and K102s Peptides with α-Synuclein for Understanding the Anti-Aggregation Mechanism: An In silico Study

Background: α-Synuclein has become the main therapeutic target in Parkinson's disease and related Synucleinopathies since the discovery of genetic associations between α-Synuclein and Parkinson's disease risk and the identification of aggregated α-Synuclein as the primary protein con-stituent of Lewy pathology two decades ago. The two new peptides K84s (FLVWGCLRGSAI-GECVVHGGPPSRH) and K102s (FLKRWARSTRWGTASCGGS) have recently been found to significantly reduce the oligomerization and aggregation of α-Synuclein. However, it is still unclear where these peptides interact with α-Synuclein at the moment. Objective: To examine the locations where K84s and K102s interact with α-Synuclein. Methods: In this investigation, the PEPFOLD3 server was used to generate the 3-D structures of the K84s and K102s peptides. Using the PatchDock web server, the two peptides were docked to the α-Synuclein molecule. After that, 50 ns of Molecular Dynamics (MD) simulations using the Am-berff99SBildn force field were performed on the two resulting docked complexes. The two com-plexes' structure, dynamics, energy profiles, and binding modes were identified through analysis of the respective MD simulation trajectories. By submitting the two complexes' lowest energy struc-ture to the PDBsum website, the interface residues in the two complexes were identified. The per residue energy decomposition (PRED) analysis using the MM-GBSA technique was used to calcu-late the contributions of each residue in the α-Synuclein of (α-Synuclein-K84s/K102s) complexes to the total binding free energy. Results: The binding of the two peptides with the α-Synuclein was demonstrated to have high bind-ing free energy. The binding free energies of the (α-Synuclein-K84s) and (α-Synuclein-K102s) complexes are -33.61 kcal/mol and -40.88 kcal/mol respectively. Using PDBsum server analysis, it was determined that in the (α-Synuclein-K84s) complex, the residues GLY 25, ALA 29, VAL 49, LEU 38, VAL 40, GLU 28, GLY 47, LYS 32, GLU 35, GLY 36, TYR 39, VAL 48 and VAL 26 (from α-Synuclein) and SER 23, LEU 7, ILE 12, HIS 25, PHE 1, HIS 18, CYS 6, ARG 24, PRO 21 and ARG 8 (from K84s peptide) were identified to be present at the interface. In the (α-Synuclein-K102s) complex, the residues VAL 40, GLY 36, GLU 35, TYR 39, LYS 45, LEU 38, LYS 43, VAL 37, THR 44, VAL 49, VAL 48, and GLU 46 (from α-Synuclein) and ARG 10, GLY 12, GLY 18, SER 15, THR 13, SER 19, TRP 11, ALA 14, CYS 16, ARG 7, ARG 4 and GLY 17 (from K102s peptide) were identified to be present at the interface. The PRED analysis revealed that the residues PHE 1, LEU 7, ILE 12, LEU 2, VAL 3, GLY 5, and PRO 21 of the K84s peptide and resi-dues VAL 48, ALA 29, VAL 40, TYR 39, VAL 49, VAL 26 and GLY 36 of α-Synuclein in the (α-Synuclein-K84s) complex are responsible for the intermolecular interaction. The residues ARG 4, ARG 10, TRP 11, ALA 14, SER 15, CYS 16 and SER 19 of the K102s peptide and residues GLU 46, LYS 45, VAL 49, GLU 35, VAL 48, TYR 39, and VAL 40 of α-Synuclein are responsible for the intermolecular interaction in the instance of the (α-Synuclein-K102s) complex. Additionally, it has been found that a sizable portion of the helical structure is preserved when α-Synuclein is in a complex form with the K84s and K102s peptides. Conclusion: Taken together the data implies that the two new peptides investigated here could be suitable candidates for future therapeutic development against α-Synuclein aggregation.

Studying the efficacy of hydrogen plasma treatment for enabling the etching of thermally annealed ruthenium in chemical solutions

Ruthenium (Ru) is a noble metal and is known to be resistant to many common chemicals and mixtures. We report in this study a controlled etching/recessing of Ru via wet processing and a combination of dry and wet process using metal-free chemical mixtures. The wet etching study was performed using both as-deposited and annealed atomic layer deposited ruthenium (Ru) films. The etching rate of Ru film in periodic acid mixtures was found to decrease substantially after being annealed at elevated temperatures (300 and 420 °C). X-ray photoelectron spectroscopy results indicated that this behavior could be explained by the formation of a thin surface ruthenium oxide layer induced by the thermal anneal. A short treatment in H2-based atmosphere at elevated temperature or in H2 plasma rendered the annealed Ru film etchable, explained by the reduction of RuOx film at the surface. The increase in surface roughness after wet etching was most likely caused by the gradual formation of oxidized Ru at the surface. Similarly, non-uniform etching of Ru was also observed for damascene Ru structure with the critical dimension ranging from ∼12 to 35 nm. An alternative approach using O2-based plasma achieved a good control of the etching (recessing) depth but also generated a thin layer of “residues” close to the interface between Ru and the liner (TiN). The results from this study have demonstrated that a short H2 plasma treatment prior to the wet process using the same chemical mixtures was able to etch the modified Ru surface layer together with the interfacial residues selectively to bulk Ru.

EmPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model.

Protein-deoxyribonucleic acid (DNA) interactions are important in a variety of biological processes. Accurately predicting protein-DNA binding affinity has been one of the most attractive and challenging issues in computational biology. However, the existing approaches still have much room for improvement. In this work, we propose an ensemble model for Protein-DNA Binding Affinity prediction (emPDBA), which combines six base models with one meta-model. The complexes are classified into four types based on the DNA structure (double-stranded or other forms) and the percentage of interface residues. For each type, emPDBA is trained with the sequence-based, structure-based and energy features from binding partners and complex structures. Through feature selection by the sequential forward selection method, it is found that there do exist considerable differences in the key factors contributing to intermolecular binding affinity. The complex classification is beneficial for the important feature extraction for binding affinity prediction. The performance comparison of our method with other peer ones on the independent testing dataset shows that emPDBA outperforms the state-of-the-art methods with the Pearson correlation coefficient of 0.53 and the mean absolute error of 1.11kcal/mol. The comprehensive results demonstrate that our method has a good performance for protein-DNA binding affinity prediction. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/emPDBA/.

Alleviating the trade-off by site-guided function switch of nitrilase to nitrile hydratase

Catalytic promiscuity makes nitrilase-mediated unnatural reactivity a promising pathway in the production of primary amides. However, during the functional switch of nitrilase, a trade-off between the newly developed hydration activity and relative activity hinders the application of nitrilase in the production of amides. Our previous study showed that the mutant F193N (Mut0) of nitrilase NIT6803 from Synechocystis sp. PCC6803 improved the hydration activity as the amide ratio increased to 73.4%, whereas the relative activity was only 50.1% of that of the wild-type (WT). In this study, we succeeded in alleviating the trade-off of Mut0 through an evolutionary combination of the key identified sites 101, 192, and 201. Inside the active pocket, the co-evolution of residue Q192 was identified by hotspot prediction to correlate with N193, and Q192H exhibited an improved amide ratio of 76.9% with a relative activity of up to 72%. Outside the active pocket, the flexible loop surface residue G101 and homodimer “A” interface residue I201 were identified using sequence alignment-based mutations. Further evolutionary combination of the three key sites yielded the best triple mutant G101K/Q192H/I201M, which had the highest amide ratio of 98.5% and considerably recovered relative activity of 82.6% while retaining only 1.5% of the carboxylic acid ratio. The transition-state analogue sheds light on the basis of the almost identical switch of the reaction pathways, and our results expand the toolbox of nitrilase-catalysed promiscuity for amide formation.

Conformational Dynamics of the Activated GLP-1 Receptor-Gs Complex Revealed by Cross-Linking Mass Spectrometry and Integrative Structure Modeling.

Despite advances in characterizing the structures and functions of G protein-coupled receptors (GPCRs), our understanding of GPCR activation and signaling is still limited by the lack of information on conformational dynamics. It is particularly challenging to study the dynamics of GPCR complexes with their signaling partners because of their transient nature and low stability. Here, by combining cross-linking mass spectrometry (CLMS) with integrative structure modeling, we map the conformational ensemble of an activated GPCR-G protein complex at near-atomic resolution. The integrative structures describe heterogeneous conformations for a high number of potential alternative active states of the GLP-1 receptor-Gs complex. These structures show marked differences from the previously determined cryo-EM structure, especially at the receptor-Gs interface and in the interior of the Gs heterotrimer. Alanine-scanning mutagenesis coupled with pharmacological assays validates the functional significance of 24 interface residue contacts only observed in the integrative structures, yet absent in the cryo-EM structure. Through the integration of spatial connectivity data from CLMS with structure modeling, our study provides a new approach that is generalizable to characterizing the conformational dynamics of GPCR signaling complexes.