Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

Efficient Generation of Conformer Ensembles Using Internal Coordinates and a Generative Directional Graph Convolution Neural Network

We present a neural-network-based high-throughput molecular conformer-generation algorithm. A chemical graph-convolutional network is trained to predict low-energy conformers in internal coordinate representation (bond lengths, bond, and torsion angles), starting from two-dimensional (2D) chemical topology. Generative neural network (NN) architecture performs denoising from torsion space, producing conformer ensembles with populations that are well correlated with torsion energy profiles. Short force-field-based energy minimization is applied to refine final conformers. All computation-intensive stages of the algorithm are GPU-optimized. The procedure (termed GINGER) is benchmarked on a commonly used test set of bioactive three-dimensional (3D) conformers from the PDB. We demonstrate highly competitive results in conformer recovery and throughput rates suitable for giga-scale compound library processing. A web server that allows interactive conformer ensemble generation by GINGER and their viewing is made freely available at https://www.molsoft.com/gingerdemo.html.

PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.

Identification of a Putative α-synuclein Radioligand Using an in silico Similarity Search

PURPOSE

Previous studies from our lab utilized an ultra-high throughput screening method to identify compound 1 as a small molecule that binds to alpha-synuclein (α-synuclein) fibrils. The goal of the current study was to conduct a similarity search of 1 to identify structural analogs having improved in vitro binding properties for this target that could be labeled with radionuclides for both in vitro and in vivo studies for measuring α-synuclein aggregates.

METHODS

Using 1 as a lead compound in a similarity search, isoxazole derivative 15 was identified to bind to α-synuclein fibrils with high affinity in competition binding assays. A photocrosslinkable version was used to confirm binding site preference. Derivative 21, the iodo-analog of 15, was synthesized, and subsequently radiolabeled isotopologs [125I]21 and [11C]21 were successfully synthesized for use in in vitro and in vivo studies, respectively. [125I]21 was used in radioligand binding studies in post-mortem Parkinson's disease (PD) and Alzheimer's disease (AD) brain homogenates. In vivo imaging of an α-synuclein mouse model and non-human primates was performed with [11C]21.

RESULTS

In silico molecular docking and molecular dynamic simulation studies for a panel of compounds identified through a similarity search, were shown to correlate with Ki values obtained from in vitro binding studies. Improved affinity of isoxazole derivative 15 for α-synuclein binding site 9 was indicated by photocrosslinking studies with CLX10. Design and successful (radio)synthesis of iodo-analog 21 of isoxazole derivative 15 enabled further in vitro and in vivo evaluation. Kd values obtained in vitro with [125I]21 for α-synuclein and Aβ42 fibrils were 0.48 ± 0.08 nM and 2.47 ± 1.30 nM, respectively. [125I]21 showed higher binding in human postmortem PD brain tissue compared with AD tissue, and low binding in control brain tissue. Lastly, in vivo preclinical PET imaging showed elevated retention of [11C]21 in PFF-injected mouse brain. However, in PBS-injected control mouse brain, slow washout of the tracer indicates high non-specific binding. [11C]21 showed high initial brain uptake in a healthy non-human primate, followed by fast washout that may be caused by rapid metabolic rate (21% intact [11C]21 in blood at 5 min p.i.).

CONCLUSION

Through a relatively simple ligand-based similarity search, we identified a new radioligand that binds with high affinity (<10 nM) to α-synuclein fibrils and PD tissue. Although the radioligand has suboptimal selectivity for α-synuclein towards Aβ and high non-specific binding, we show here that a simple in silico approach is a promising strategy to identify novel ligands for target proteins in the CNS with the potential to be radiolabeled for PET neuroimaging studies.

Biological response and cell death signaling pathways modulated by tetrahydroisoquinoline-based aldoximes in human cells

The uncharged 3-hydroxy-2-pyridine aldoximes with protonatable tertiary amines are studied as antidotes in toxic organophosphates (OP) poisoning. Due to some of their specific structural features, we hypothesize that these compounds could exert diverse biological activity beyond their main scope of application. To examine this further, we performed an extensive cell-based assessment to determine their effects on human cells (SH-SY5Y, HEK293, HepG2, HK-2, myoblasts and myotubes) and possible mechanism of action. As our results indicated, aldoxime having a piperidine moiety did not induce significant toxicity up to 300µM within 24hours, while those with a tetrahydroisoquinoline moiety, in the same concentration range, showed time-dependent effects and stimulated mitochondria-mediated activation of the intrinsic apoptosis pathway through ERK1/2 and p38-MAPK signaling and subsequent activation of initiator caspase 9 and executive caspase 3 accompanied with DNA damage as observed already after 4hour exposure. Mitochondria and fatty acid metabolism were also likely targets of 3-hydroxy-2-pyridine aldoximes with tetrahydroisoquinoline moiety, due to increased phosphorylation of acetyl-CoA carboxylase. In silico analysis predicted kinases as their most probable target class, while pharmacophores modeling additionally predicted the inhibition of a cytochrome P450cam. Overall, if the absence of significant toxicity for piperidine bearing aldoxime highlights the potential of its further studies in medical counter-measures, the observed biological activity of aldoximes with tetrahydroisoquinoline moiety could be indicative for future design of compounds either in a negative context in OP antidotes design, or in a positive one for design of compounds for the treatment of other phenomena like cell proliferating malignancies.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Targeting protein conformations with small molecules to control protein complexes

Dynamic protein complexes function in all cellular processes, from signaling to transcription, using distinct conformations that regulate their activity. Conformational switching of proteins can turn on or off their activity through protein-protein interactions, catalytic function, cellular localization, or membrane interaction. Recent advances in structural, computational, and chemical methodologies have enabled the discovery of small-molecule activators and inhibitors of conformationally dynamic proteins by using a more rational design than a serendipitous screening approach. Here, we discuss such recent examples, focusing on the mechanism of protein conformational switching and its regulation by small molecules. We emphasize the rational approaches to control protein oligomerization with small molecules that offer exciting opportunities for investigation of novel biological mechanisms and drug discovery.

DNAJA1- and conformational mutant p53-dependent inhibition of cancer cell migration by a novel compound identified through a virtual screen

Cancers are frequently addicted to oncogenic missense mutant p53 (mutp53). DNAJA1, a member of heat shock protein 40 (HSP40), also known as J-domain proteins (JDPs), plays a crucial role in the stabilization and oncogenic activity of misfolded or conformational mutp53 by binding to and preventing mutp53 from proteasomal degradation. However, strategies to deplete mutp53 are not well-established, and no HSP40/JDPs inhibitors are clinically available. To identify compounds that bind to DNAJA1 and induce mutp53 degradation, we performed an in silico docking study of ~10 million of compounds from the ZINC database for the J-domain of DNAJA1. A compound 7-3 was identified, and its analogue A11 effectively reduced the levels of DNAJA1 and conformational mutp53 with minimal effects on the levels of wild-type p53 and DNA-contact mutp53. A11 suppressed migration and filopodia formation in a manner dependent on DNAJA1 and conformational mutp53. A mutant DNAJA1 with alanine mutations at predicted amino acids (tyrosine 7, lysine 44, and glutamine 47) failed to bind to A11. Cells expressing the mutant DNAJA1 became insensitive to A11-mediated depletion of DNAJA1 and mutp53 as well as A11-mediated inhibition of cell migration. Thus, A11 is the first HSP40/JDP inhibitor that has not been previously characterized for depleting DNAJA1 and subsequently conformational mutp53, leading to inhibition of cancer cell migration. A11 can be exploited for a novel treatment against cancers expressing conformational mutp53.

Virtual Screening of Drug Proteins Based on the Prediction Classification Model of Imbalanced Data Mining

We propose a virtual screening method based on imbalanced data mining in this paper, which combines virtual screening techniques with imbalanced data classification methods to improve the traditional virtual screening process. First, in the actual virtual screening process, we apply k-means and smote heuristic oversampling method to deal with imbalanced data. Meanwhile, to enhance the accuracy of the virtual screening process, a particle swarm optimization algorithm is introduced to optimize the parameters of the support vector machine classifier, and the concept of ensemble learning is brought in. The classification technique based on particle swarm optimization, support vector machine and adaptive boosting is used to screen the molecular docking conformation to improve the accuracy of the prediction. Finally, in the experimental construction and analysis section, the proposed method was validated using relevant data from the protein data bank database and PubChem database. The experimental results indicated that the proposed method can effectively improve the accuracy of virus screening and has practical guidance for new drug development. This research regards virtual screening as a problem of imbalanced data classification, which has obvious guiding significance and also provides a certain reference for the problems faced by virtual screening technology.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

How Structural Biology Has Directly Impacted Our Understanding of P2X Receptor Function and Gating

P2X receptors are ATP-gated ion channels expressed in a wide variety of eukaryotic cells. They play key roles in diverse processes such as platelet activation, smooth muscle contraction, synaptic transmission, nociception, cell proliferation, and inflammation making this receptor family an important pharmacological target. Structures of P2X receptors solved by X-ray crystallography have been instrumental in helping to define mechanisms of molecular P2X receptor function. In 2009, the first X-ray structure of the P2X4 receptor subtype confirmed a trimeric stoichiometry and revealed the overall architecture of the functional ion channel. Subsequent X-ray structures have provided the molecular details to define the orthosteric ATP binding pocket, the orthosteric antagonist binding pocket, an allosteric antagonist binding pocket, and the pore architecture in each of the major conformational states of the receptor gating cycle. Moreover, the unique gating mechanism by which P2X receptor subtypes desensitize at differing rates, referred to as the helical recoil model of receptor desensitization, was discovered directly from X-ray structures of the P2X3 receptor. However, structures of P2X receptors solved by X-ray crystallography have only been able to provide limited information on the cytoplasmic domain of this receptor family, as this domain was always truncated to varying degrees in order to facilitate crystallization. Because the P2X7 receptor subtype has a significantly larger cytoplasmic domain that has been shown to be necessary for its ability to initiate apoptosis, an absence of structural information on the P2X7 receptor cytoplasmic domain has limited our understanding of its complex signaling pathways as well as its unusual ability to remain open without undergoing desensitization. This absence of cytoplasmic structural information for P2X7 receptors was recently overcome when the first full-length P2X7 receptor structures were solved by single-particle cryogenic electron microscopy. These structures finally provide insight into the large and unique P2X7 receptor cytoplasmic domain and revealed two novel structural elements and several surprising findings: first, a cytoplasmic structural element called the cytoplasmic ballast was identified that contains a dinuclear zinc ion complex and a high affinity guanosine nucleotide binding site and second, a palmitoylated membrane proximal structural element called the C-cys anchor was identified which prevents P2X7 receptor desensitization. This chapter will highlight the major structural and functional aspects of P2X receptors discovered through structural biology, with a key emphasis on the most recent cryogenic electron microscopy structures of the full-length, wild-type P2X7 receptor.

Alpha-Synuclein PET Tracer Development-An Overview about Current Efforts

Neurodegenerative diseases such as Parkinson’s disease (PD) are manifested by inclusion bodies of alpha-synuclein (α-syn) also called α-synucleinopathies. Detection of these inclusions is thus far only possible by histological examination of postmortem brain tissue. The possibility of non-invasively detecting α-syn will therefore provide valuable insights into the disease progression of α-synucleinopathies. In particular, α-syn imaging can quantify changes in monomeric, oligomeric, and fibrillic α-syn over time and improve early diagnosis of various α-synucleinopathies or monitor treatment progress. Positron emission tomography (PET) is a non-invasive in vivo imaging technique that can quantify target expression and drug occupancies when a suitable tracer exists. As such, novel α-syn PET tracers are highly sought after. The development of an α-syn PET tracer faces several challenges. For example, the low abundance of α-syn within the brain necessitates the development of a high-affinity ligand. Moreover, α-syn depositions are, in contrast to amyloid proteins, predominantly localized intracellularly, limiting their accessibility. Furthermore, another challenge is the ligand selectivity over structurally similar amyloids such as amyloid-beta or tau, which are often co-localized with α-syn pathology. The lack of a defined crystal structure of α-syn has also hindered rational drug and tracer design efforts. Our objective for this review is to provide a comprehensive overview of current efforts in the development of selective α-syn PET tracers.

Fragment-Based Ligand Discovery Using Protein-Observed 19F NMR: A Second Semester Organic Chemistry CURE Project

Curriculum-based undergraduate research experiences (CUREs) have been shown to increase student retention in STEM fields and are starting to become more widely adopted in chemistry curricula. Here we describe a 10-week CURE that is suitable for a second-semester organic chemistry laboratory course. Students synthesize small molecules and use protein-observed ¹⁹F (PrOF) NMR to assess the small molecule's binding affinity to a target protein. The research project introduced students to multistep organic synthesis, structure-activity relationship studies, quantitative biophysical measurements (measuring K_d from PrOF NMR experiments), and scientific literacy. Docking experiments could be added to help students understand how changes in a ligand structure may affect binding to a protein. Assessment using the CURE survey indicates self-perceived skill gains from the course that exceed gains measured in a traditional and an inquiry-based laboratory experience. Given the speed of the binding experiment and the alignment of the synthetic methods with a second-semester organic chemistry laboratory course, a PrOF NMR fragment-based ligand discovery lab can be readily implemented in the undergraduate chemistry curriculum.

Deep learning model to predict complex stress and strain fields in hierarchical composites

Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory-based conditional generative adversarial neural network (cGAN), to bridge the gap between a material's microstructure-the design space-and physical performance. Our end-to-end deep learning model predicts physical fields like stress or strain directly from the material microstructure geometry, and reaches an astonishing accuracy not only for predicted field data but also for derivative material property predictions. Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of component shapes, boundary conditions, and geometrical hierarchy, providing perspectives of performing physical modeling and simulations. The method vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.

Identification of a nanomolar affinity α-synuclein fibril imaging probe by ultra-high throughput in silico screening

Small molecules that bind with high affinity and specificity to fibrils of the α-synuclein (αS) protein have the potential to serve as positron emission tomography (PET) imaging probes to aid in the diagnosis of Parkinson's disease and related synucleinopathies. To identify such molecules, we employed an ultra-high throughput in silico screening strategy using idealized pseudo-ligands termed exemplars to identify compounds for experimental binding studies. For the top hit from this screen, we used photo-crosslinking to confirm its binding site and studied the structure-activity relationship of its analogs to develop multiple molecules with nanomolar affinity for αS fibrils and moderate specificity for αS over Aβ fibrils. Lastly, we demonstrated the potential of the lead analog as an imaging probe by measuring binding to αS-enriched homogenates from mouse brain tissue using a radiolabeled analog of the identified molecule. This study demonstrates the validity of our powerful new approach to the discovery of PET probes for challenging molecular targets.

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery

The theoretical structure prediction method via quantum mechanical atomistic simulations such as density functional theory (DFT), based solely on chemical composition, has already become a routine tool to determine the structures of physical and chemical systems, e.g., solids and clusters. However, the application of DFT to more realistic simulations, to a large extent, is impeded because of the unfavorable scaling of the computational cost with respect to the system size. During recent years, the machine learning potential (MLP) method has been rapidly rising as an accurate and efficient tool for atomistic simulations. In this Perspective, we provide an introduction to the basic principles and advantages of the combination of structure prediction and MLP, as well as the challenges and opportunities associated with this promising approach.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception, and Virtual Screening

Discovering the very first ligands of pharmacologically important targets in a fast and cost-efficient manner is an important issue in drug discovery. In the absence of structural information on either endogenous or synthetic ligands, computational chemists classically identify the very first hits by docking compound libraries to a binding site of interest, with well-known biases arising from the usage of scoring functions. We herewith propose a novel computational method tailored to ligand-free protein structures and consisting in the generation of simple cavity-based pharmacophores to which potential ligands could be aligned by the use of a smooth Gaussian function. The method, embedded in the IChem toolkit, automatically detects ligand-binding cavities, then predicts their structural druggability, and last creates a structure-based pharmacophore for predicted druggable binding sites. A companion tool (Shaper2) was designed to align ligands to cavity-derived pharmacophoric features. The proposed method is as efficient as state-of-the-art virtual screening methods (ROCS, Surflex-Dock) in both posing and virtual screening challenges. Interestingly, IChem-Shaper2 is clearly orthogonal to these latter methods in retrieving unique chemotypes from high-throughput virtual screening data.

Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks

NP screening through a deep learning approach against Anti-EGFR and validation through docking with AuNP. Biochemical pathway and simulation of AuNP with Anti-EGFR and further implementation in biological circuits.

Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex

Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS-TLR4-MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein-protein interaction (PPI) in TLR4-MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4-MD-2) and dimerization (MD-2-TLR4*) protein-protein interaction interfaces in TLR4-MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4-MD-2 protein-protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4-MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.

Benchmark Study Based on 2P2IDB to Gain Insights into the Discovery of Small-Molecule PPI Inhibitors

Protein-protein interactions (PPIs) have been regarded as novel and highly promising drug targets in drug discovery. Numerous new experimental techniques and computational approaches have been developed to assist the design of PPI modulators during the past two decades. However, identification and optimization of small-molecule inhibitors targeting PPIs is still a particularly challenging task due to the "undruggable" profiles of PPI interfaces. Nowadays, in silico screening, especially docking-based virtual screening, has emerged as an effective method to complement experimental high-throughput screening in identifying novel and potent small-molecule PPI inhibitors. Here, on the basis of the 2P2I_DB database, we explored the structural features of the known small-molecule PPI inhibitors and analyzed the characteristics of the PPI binding pockets. More importantly, we evaluated the sampling power and screening power of six popular docking programs for PPI targets. Our results indicate that the chlorinated conjugate group and amidelike linkage are two types of privileged fragments of PPI inhibitors; the average druggability of the binding sites of the PPI targets in 2P2IDB is slightly worse than that of traditional ones; both academic and commercial docking programs exhibit an acceptable accuracy on pose prediction for PPI inhibitors, but their screening powers for identifying PPI inhibitors are still not satisfactory. It is expected that our work can provide valuable guidance on the construction of PPI-focused library, the determination of druggable PPI binding pocket, and the selection of docking program for the screening of small-molecule PPI inhibitors.

Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pK_a determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.

G.A.M.E.: GPU-accelerated mixture elucidator

GPU acceleration is useful in solving complex chemical information problems. Identifying unknown structures from the mass spectra of natural product mixtures has been a desirable yet unresolved issue in metabolomics. However, this elucidation process has been hampered by complex experimental data and the inability of instruments to completely separate different compounds. Fortunately, with current high-resolution mass spectrometry, one feasible strategy is to define this problem as extending a scaffold database with sidechains of different probabilities to match the high-resolution mass obtained from a high-resolution mass spectrum. By introducing a dynamic programming (DP) algorithm, it is possible to solve this NP-complete problem in pseudo-polynomial time. However, the running time of the DP algorithm grows by orders of magnitude as the number of mass decimal digits increases, thus limiting the boost in structural prediction capabilities. By harnessing the heavily parallel architecture of modern GPUs, we designed a “compute unified device architecture” (CUDA)-based GPU-accelerated mixture elucidator (G.A.M.E.) that considerably improves the performance of the DP, allowing up to five decimal digits for input mass data. As exemplified by four testing datasets with verified constitutions from natural products, G.A.M.E. allows for efficient and automatic structural elucidation of unknown mixtures for practical procedures.Graphical abstract

Non-Invasive Monitoring of CNS MHC-I Molecules in Ischemic Stroke Mice

Ischemic stroke is one of the leading causes of morbidity and mortality worldwide. The expression of major histocompatibility complex class I (MHC-I) molecules in the central nervous system, which are silenced under normal physiological conditions, have been reported to be induced by injury stimulation. The purpose of this study was to determine whether MHC-I molecules could serve as molecular targets for the acute phase of ischemic stroke and to assess whether a high-affinity peptide specific for MHC-I molecules could be applied in the near-infrared imaging of cerebral ischemic mice. Quantitative real-time PCR and Western blotting were used to detect the expression of MHC-I molecules in two mouse models of cerebral ischemic stroke and an in vitro model of ischemia. The NetMHC 4.0 server was used to screen a high-affinity peptide specific for mouse MHC-I molecules. The Rosetta program was used to identify the specificity and affinity of the screened peptide (histocompatibility-2 binding peptide, H2BP). The results demonstrated that MHC-I molecules could serve as molecular targets for the acute phase of ischemic stroke. Cy5.5-H2BP molecular probes could be applied in the near-infrared imaging of cerebral ischemic mice. Research on the expression of MHC-I molecules in the acute phase after ischemia and MHC-I-targeted imaging may not only be helpful for understanding the mechanism of ischemic and hypoxic brain injury and repair but also has potential application value in the imaging of ischemic stroke.

The impact of in silico screening in the discovery of novel and safer drug candidates

Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.

Discovery of Nicotinamide Adenine Dinucleotide Binding Proteins in the Escherichia coli Proteome Using a Combined Energetic- and Structural-Bioinformatics-Based Approach

Protein-ligand interaction plays a critical role in regulating the biochemical functions of proteins. Discovering protein targets for ligands is vital to new drug development. Here, we present a strategy that combines experimental and computational approaches to identify ligand-binding proteins in a proteomic scale. For the experimental part, we coupled pulse proteolysis with filter-assisted sample preparation (FASP) and quantitative mass spectrometry. Under denaturing conditions, ligand binding affected protein stability, which resulted in altered protein abundance after pulse proteolysis. For the computational part, we used the software Patch-Surfer2.0. We applied the integrated approach to identify nicotinamide adenine dinucleotide (NAD)-binding proteins in the Escherichia coli proteome, which has over 4200 proteins. Pulse proteolysis and Patch-Surfer2.0 identified 78 and 36 potential NAD-binding proteins, respectively, including 12 proteins that were consistently detected by the two approaches. Interestingly, the 12 proteins included 8 that are not previously known as NAD binders. Further validation of these eight proteins showed that their binding affinities to NAD computed by AutoDock Vina are higher than their cognate ligands and also that their protein ratios in the pulse proteolysis are consistent with known NAD-binding proteins. These results strongly suggest that these eight proteins are indeed newly identified NAD binders.

Building Compound Archives for the Future

Will the targets of the future be covered by the compound libraries of today? This communication will cover a critical review of past strategies before turning to a new measure of diversity, protein pockets. A fingerprint descriptor for pockets will be described.