A remarkable genetic shift in a transmitted/founder virus broadens antibody responses against HIV-1.

A productive HIV-1 infection in humans is often established by transmission and propagation of a single transmitted/founder (T/F) virus, which then evolves into a complex mixture of variants during the lifetime of infection. An effective HIV-1 vaccine should elicit broad immune responses in order to block the entry of diverse T/F viruses. Currently, no such vaccine exists. An in-depth study of escape variants emerging under host immune pressure during very early stages of infection might provide insights into such a HIV-1 vaccine design. Here, in a rare longitudinal study involving HIV-1 infected individuals just days after infection in the absence of antiretroviral therapy, we discovered a remarkable genetic shift that resulted in near complete disappearance of the original T/F virus and appearance of a variant with H173Y mutation in the variable V2 domain of the HIV-1 envelope protein. This coincided with the disappearance of the first wave of strictly H173-specific antibodies and emergence of a second wave of Y173-specific antibodies with increased breadth. Structural analyses indicated conformational dynamism of the envelope protein which likely allowed selection of escape variants with a conformational switch in the V2 domain from an α-helix (H173) to a ß-strand (Y173) and induction of broadly reactive antibody responses. This differential breadth due to a single mutational change was also recapitulated in a mouse model. Rationally designed combinatorial libraries containing 54 conformational variants of V2 domain around position 173 further demonstrated increased breadth of antibody responses elicited to diverse HIV-1 envelope proteins. These results offer new insights into designing broadly effective HIV-1 vaccines.

PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors.

Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.

Discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for treating cancers with microsatellite instability.

Microsatellite instability (MSI) is a hypermutable condition caused by DNA mismatch repair system defects, contributing to the development of various cancer types. Recent research has identified Werner syndrome ATP-dependent helicase (WRN) as a promising synthetic lethal target for MSI cancers. Herein, we report the first discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for MSI cancer therapy. Initial computational analysis and biological evaluation identified a new scaffold for a WRN inhibitor. Subsequent SAR study led to the discovery of a highly potent WRN inhibitor. Furthermore, we demonstrated that the optimal compound induced DNA damage and apoptotic cell death in MSI cancer cells by inhibiting WRN. This study provides a new pharmacophore for WRN inhibitors, emphasizing their therapeutic potential for MSI cancers.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches.

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

S-Pred: protein structural property prediction using MSA transformer.

Predicting the local structural features of a protein from its amino acid sequence helps its function prediction to be revealed and assists in three-dimensional structural modeling. As the sequence-structure gap increases, prediction methods have been developed to bridge this gap. Additionally, as the size of the structural database and computing power increase, the performance of these methods have also significantly improved. Herein, we present a powerful new tool called S-Pred, which can predict eight-state secondary structures (SS8), accessible surface areas (ASAs), and intrinsically disordered regions (IDRs) from a given sequence. For feature prediction, S-Pred uses multiple sequence alignment (MSA) of a query sequence as an input. The MSA input is converted to features by the MSA Transformer, which is a protein language model that uses an attention mechanism. A long short-term memory (LSTM) was employed to produce the final prediction. The performance of S-Pred was evaluated on several test sets, and the program consistently provided accurate predictions. The accuracy of the SS8 prediction was approximately 76%, and the Pearson's correlation between the experimental and predicted ASAs was 0.84. Additionally, an IDR could be accurately predicted with an F1-score of 0.514. The program is freely available at https://github.com/arontier/S_Pred_Paper and https://ad3.io as a code and a web server.

Synthesis of 4-substituted benzyl-2-triazole-linked-tryptamine-paeonol derivatives and evaluation of their selective inhibitions against butyrylcholinesterase and monoamine oxidase-B.

Cholinesterase (ChE) and monoamine oxidase (MAO) inhibitors are being used and developed to treat Alzheimer's disease (AD), a major type of dementia patients. Fifteen 4-substituted benzyl-2-triazole-linked-tryptamine-paeonol derivatives were synthesized and evaluated for their inhibitory activities against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), monoamine oxidase-A (MAO-A), and B (MAO-B). Compound 896 was the most potent BChE inhibitor (IC50 = 0.13 µM) with the selectivity index (SI) value of >769.23 for BChE over AChE. Compound 897 was the most potent selective MAO-B inhibitor (IC50 = 0.73 µM; SI = 20.45 for MAO-B over MAO-A). The meta-CF3 substituent of 896 increased BChE inhibitory activity and the para-CF3 substituent of 897 increased MAO-B inhibitory activity. Compound 896 was a reversible noncompetitive BChE inhibitor (Ki = 0.171 µM) and 897 was a reversible competitive MAO-B inhibitor (Ki = 0.237 µM). Compound 896 had a lower binding energy (-13.75 kcal/mol) to BChE than 897 (-11.29 kcal/mol), and 897 had a lower binding energy to MAO-B (-11.31 kcal/mol) than that to MAO-A (-6.72 kcal/mol). Little cytotoxicity was observed for 896 and 897 to normal cells (MDCK) and human neuroblastoma cells (SH-SY5Y). This study suggested that 896 and 897 are therapeutic candidates for various neurodegenerative disorders such as AD.

Surface-based protein domains retrieval methods from a SHREC2021 challenge.

Proteins are essential to nearly all cellular mechanism and the effectors of the cells activities. As such, they often interact through their surface with other proteins or other cellular ligands such as ions or organic molecules. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence similar 3D surface properties (shape, physico-chemical properties, …). The protein surfaces are therefore of primary importance for their activity. In the present work, we assess the ability of different methods to detect such similarities based on the geometry of the protein surfaces (described as 3D meshes), using either their shape only, or their shape and the electrostatic potential (a biologically relevant property of proteins surface). Five different groups participated in this contest using the shape-only dataset, and one group extended its pre-existing method to handle the electrostatic potential. Our comparative study reveals both the ability of the methods to detect related proteins and their difficulties to distinguish between highly related proteins. Our study allows also to analyze the putative influence of electrostatic information in addition to the one of protein shapes alone. Finally, the discussion permits to expose the results with respect to ones obtained in the previous contests for the extended method. The source codes of each presented method have been made available online.

Discovery of a Potent Candidate for RET-Specific Non-Small-Cell Lung Cancer-A Combined In Silico and In Vitro Strategy.

Rearranged during transfection (RET) is a tyrosine kinase oncogenic receptor, activated in several cancers including non-small-cell lung cancer (NSCLC). Multiple kinase inhibitors vandetanib and cabozantinib are commonly used in the treatment of RET-positive NSCLC. However, specificity, toxicity, and reduced efficacy limit the usage of multiple kinase inhibitors in targeting RET protein. Thus, in the present investigation, we aimed to figure out novel and potent candidates for the inhibition of RET protein using combined in silico and in vitro strategies. In the present study, screening of 11,808 compounds from the DrugBank repository was accomplished by different hypotheses such as pharmacophore, e-pharmacophore, and receptor cavity-based models in the initial stage. The results from the different hypotheses were then integrated to eliminate the false positive prediction. The inhibitory activities of the screened compounds were tested by the glide docking algorithm. Moreover, RF score, Tanimoto coefficient, prime-MM/GBSA, and density functional theory calculations were utilized to re-score the binding free energy of the docked complexes with high precision. This procedure resulted in three lead molecules, namely DB07194, DB03496, and DB11982, against the RET protein. The screened lead molecules together with reference compounds were then subjected to a long molecular dynamics simulation with a 200 ns time duration to validate the inhibitory activity. Further analysis of compounds using MM-PBSA and mutation studies resulted in the identification of potent compound DB07194. In essence, a cell viability assay with RET-specific lung cancer cell line LC-2/ad was also carried out to confirm the in vitro biological activity of the resultant compound, DB07194. Indeed, the results from our study conclude that DB07194 can be effectively translated for this new therapeutic purpose, in contrast to the properties for which it was originally designed and synthesized.

Exploring safe and potent bioactives for the treatment of non-small cell lung cancer.

Activating and suppressing mutations in the MAPK pathway receptors are the primary causes of NSCLC. Of note, MEK inhibition is considered a promising strategy because of the diverse structures and harmful effects of upstream receptors in MAPK pathway. Thus, we explore a total of 1574 plant-based bioactive compounds activity against MEK using an energy-based virtual screening strategy. Molecular docking, binding free energy, and drug-likeness analysis were performed through GLIDE, Prime MM-GBSA, and QikProp module, respectively. The findings indicate that 5-O-caffeoylshikimic acid has an increased binding affinity to MEK protein. Further, molecular dynamic simulations and MM-PBSA analysis were performed to explore the ligand activity in real-life situations. In essence, compounds inhibitory activity was validated across 77 lung cancer cell lines using multimodal attention-based neural network algorithm. Eventually, our analysis highlight that 5-O-caffeoylshikimic acid obtained from the bark of Rhizoma smilacis glabrae would be developed as a potential compound for treating NSCLC.

Current progress and future perspectives of polypharmacology : From the view of non-small cell lung cancer.

A pre-eminent subtype of lung carcinoma, Non-small cell lung cancer accounts for paramount causes of cancer-associated mortality worldwide. Undeterred by the endeavour in the treatment strategies, the overall cure and survival rates for NSCLC remain substandard, particularly in metastatic diseases. Moreover, the emergence of resistance to classic anticancer drugs further deteriorates the situation. These demanding circumstances culminate the need of extended and revamped research for the establishment of upcoming generation cancer therapeutics. Drug repositioning introduces an affordable and efficient strategy to discover novel drug action, especially when integrated with recent systems biology driven stratagem. This review illustrates the trendsetting approaches in repurposing along with their numerous success stories with an emphasize on the NSCLC therapeutics. Indeed, these novel hits, in combination with conventional anticancer agents, will ideally make their way the clinics and strengthen the therapeutic arsenal to combat drug resistance in the near future.

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks.

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

Current Challenges and Opportunities in Designing Protein-Protein Interaction Targeted Drugs.

It has been noticed that the efficiency of drug development has been decreasing in the past few decades. To overcome the situation, protein-protein interactions (PPIs) have been identified as new drug targets as early as 2000. PPIs are more abundant in human cells than single proteins and play numerous important roles in cellular processes including diseases. However, PPIs have very different physicochemical features from the conventional drug targets, which make targeting PPIs challenging. Therefore, as of now, only a small number of PPI inhibitors have been approved or progressed to a stage of clinical trial. In this article, we first overview previous works that analyzed differences between PPIs with PPI targeting ligands and conventional drugs with their binding pockets. Then, we constructed an up-to-date list of PPI targeting drugs that have been approved or are currently under clinical trial and have bound drug-target structures available. Using the dataset, we analyzed the PPIs and their ligands using several scores of druggability. Druggability scores showed that PPI sites and their drugs targeting PPIs are less druggable than conventional binding pockets and drugs, which also indicates that PPI drugs do not follow the conventional rules for drug design, such as Lipinski's rule of five. Our analyses suggest that developing a new rule would be beneficial for guiding PPI-drug discovery.

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46.

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1.

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0.

Computational prediction of protein-ligand interactions is a useful approach that aids the drug discovery process. Two major tasks of computational approaches are to predict the docking pose of a compound in a known binding pocket and to rank compounds in a library according to their predicted binding affinities. There are many computational tools developed in the past decades both in academia and industry. To objectively assess the performance of existing tools, the community has held a blind assessment of computational predictions, the Drug Design Data Resource Grand Challenge. This round, Grand Challenge 4 (GC4), focused on two targets, protein beta-secretase 1 (BACE-1) and cathepsin S (CatS). We participated in GC4 in both BACE-1 and CatS challenges using our molecular surface-based virtual screening method, PL-PatchSurfer2.0. A unique feature of PL-PatchSurfer2.0 is that it uses the three-dimensional Zernike descriptor, a mathematical moment-based shape descriptor, to quantify local shape complementarity between a ligand and a receptor, which properly incorporates molecular flexibility and provides stable affinity assessment for a bound ligand-receptor complex. Since PL-PatchSurfer2.0 does not explicitly build a bound pose of a ligand, we used an external docking program, such as AutoDock Vina, to provide an ensemble of poses, which were then evaluated by PL-PatchSurfer2.0. Here, we provide an overview of our method and report the performance in GC4.

Lactose derivatives as potential inhibitors of pectin methylesterases.

The discovery of molecules that can inhibit the action of phytopathogens is essential to find alternative to current pesticides. Pectin methylesterases (PME), enzymes that fine-tune the degree of methylesterification of plant cell wall pectins, play a key role in the pathogenicity of fungi or bacteria. Here we report the synthesis of new lactoside derivatives and their analysis as potential PME inhibitors using three plants and one fungal PME. Because of its structure, abundance and reduced cost, lactose was chosen as a case study. Lactoside derivatives were obtained by TEMPO-mediated oxidation of methyl lactoside, followed by an esterification procedure. Three derivatives were synthesized: sodium (methyl-lactosid)uronate, methyl (methyl-lactosid)uronate and butyl (methyl-lactosid)uronate. The inhibition of the plant and pathogen enzyme activities by lactoside derivatives was measured in vitro, showing the importance of the substitution on lactose: methyl (methyl-lactosid)uronate was more efficient than butyl (methyl-lactosid)uronate. These results were confirmed by docking analysis showing the difference in the interaction between lactoside derivatives and PME proteins. In conclusion, this study identified novel inhibitors of pectin remodeling enzymes.

55 Years of the Rossmann Fold.

The Rossmann fold is one of the most commonly observed structural domains in proteins. The fold is composed of consecutive alternating ß-strands and α-helices that form a layer of ß-sheet with one (or two) layer(s) of α-helices. Here, we will discuss the Rossmann fold starting from its discovery 55 years ago, then overview entries of the fold in the major protein classification databases, SCOP and CATH, as well as the number of the occurrences of the fold in genomes. We also discuss the Rossmann fold as an interesting target of protein engineering as the site-directed mutagenesis of the fold can alter the ligand-binding specificity of the structure.

Three-Dimensional Krawtchouk Descriptors for Protein Local Surface Shape Comparison.

Direct comparison of three-dimensional (3D) objects is computationally expensive due to the need for translation, rotation, and scaling of the objects to evaluate their similarity. In applications of 3D object comparison, often identifying specific local regions of objects is of particular interest. We have recently developed a set of 2D moment invariants based on discrete orthogonal Krawtchouk polynomials for comparison of local image patches. In this work, we extend them to 3D and construct 3D Krawtchouk descriptors (3DKDs) that are invariant under translation, rotation, and scaling. The new descriptors have the ability to extract local features of a 3D surface from any region-of-interest. This property enables comparison of two arbitrary local surface regions from different 3D objects. We present the new formulation of 3DKDs and apply it to the local shape comparison of protein surfaces in order to predict ligand molecules that bind to query proteins.

Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method.

Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2 . We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.