The molecular basis of IGF-II/IGF2R recognition: a combined molecular dynamics simulation, free-energy calculation and computational alanine scanning study

Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review

Drug discovery is a complex and iterative process, making it ideal for using artificial intelligence (AI). This paper uses a bibliometric approach to reveal AI's trend and underlying structure in drug discovery (AIDD). A total of 4310 journal articles and reviews indexed in Scopus were analyzed, revealing that AIDD has been rapidly growing over the past two decades, with a significant increase after 2017. The United States, China, and the United Kingdom were the leading countries in research output, with academic institutions, particularly the Chinese Academy of Sciences and the University of Cambridge, being the most productive. In addition, industrial companies, including both pharmaceutical and high-tech ones, also made significant contributions. Additionally, this paper thoroughly discussed the evolution and research frontiers of AIDD, which were uncovered through co-occurrence analyses of keywords using VOSviewer. Our findings highlight that AIDD is an interdisciplinary and promising research field that has the potential to revolutionize drug discovery. The comprehensive overview provided here will be of significant interest to researchers, practitioners, and policy-makers in related fields. The results emphasize the need for continued investment and collaboration in AIDD to accelerate drug discovery, reduce costs, and improve patient outcomes.

Targeting cryptic-orthosteric site of PD-L1 for inhibitor identification using structure-guided approach

Approved mAbs that block the protein-protein interaction (PPI) interface of the PD-1/PD-L1 immune checkpoint axis have led to significant improvements in cancer treatment. Despite having drawbacks of mAbs only few a compounds are reported till date against this axis. Inhibiting PPIs using small molecules has emerged as a significant therapeutic opportunity, demanding for the identification of drug-like molecules at an accelerated pace under the hit-to-lead campaigns. Due to the PD-L1's cross-talk with PD-1/CD80 and its overexpression on cancer cells, as well as the availability of its crystal structures with small molecules, it is an enticing therapeutic target for structure-assisted small molecule design. Furthermore, the selection of chemical databases enriched with focused designing for PPI interfaces is crucial. Therefore, in this study we have utilized the Asinex signature library for structure-assisted virtual screening to find the potential PD-L1 inhibitors by targeting the cryptic PD-L1 interface, followed by induced fit docking for pose refinements in the pocket. The obtained hits were then subjected to interaction fingerprinting and ligand-based drug-likeness investigations in order to evaluate and analyze their drug-like qualities (ADME). Twelve compounds qualified for molecular dynamics simulations, followed by thermodynamic calculations for evaluation of their stability and energetics inside the pocket. Two novel compounds with different chemical moieties have been identified that are consistent throughout the simulation, mimicking the interactions and binding energies with BMS-1166. These compounds appear as potential therapeutic candidates to be explored experimentally, thereby paving the way for the development of novel leads as immunomodulators.

Molecular Dynamics Simulations Reveal the Interaction Fingerprint of Remdesivir Triphosphate Pivotal in Allosteric Regulation of SARS-CoV-2 RdRp

The COVID-19 pandemic has now strengthened its hold on human health and coronavirus' lethal existence does not seem to be going away soon. In this regard, the optimization of reported information for understanding the mechanistic insights that facilitate the discovery towards new therapeutics is an unmet need. Remdesivir (RDV) is established to inhibit RNA-dependent RNA polymerase (RdRp) in distinct viral families including Ebola and SARS-CoV-2. Therefore, its derivatives have the potential to become a broad-spectrum antiviral agent effective against many other RNA viruses. In this study, we performed comparative analysis of RDV, RMP (RDV monophosphate), and RTP (RDV triphosphate) to undermine the inhibition mechanism caused by RTP as it is a metabolically active form of RDV. The MD results indicated that RTP rearranges itself from its initial RMP-pose at the catalytic site towards NTP entry site, however, RMP stays at the catalytic site. The thermodynamic profiling and free-energy analysis revealed that a stable pose of RTP at NTP entrance site seems critical to modulate the inhibition as its binding strength improved more than its initial RMP-pose obtained from docking at the catalytic site. We found that RTP not only occupies the residues K545, R553, and R555, essential to escorting NTP towards the catalytic site, but also interacts with other residues D618, P620, K621, R624, K798, and R836 that contribute significantly to its stability. From the interaction fingerprinting it is revealed that the RTP interact with basic and conserved residues that are detrimental for the RdRp activity, therefore it possibly perturbed the catalytic site and blocked the NTP entrance site considerably. Overall, we are highlighting the RTP binding pose and key residues that render the SARS-CoV-2 RdRp inactive, paving crucial insights towards the discovery of potent inhibitors.

Conformational dynamics is critical for the allosteric inhibition of cGAS upon acetyl-mimic mutations

Detection of cytosolic dsDNA by cyclic GMP-AMP synthase (cGAS) is critical for the immune system to sense and fight against infection, but chronic activation of cGAS by self-DNA leads to autoimmune diseases without effective treatment yet. It was found that acetylation on either Lys384, Lys394, or Lys414 could inhibit the catalytic production of cGAMP by cGAS, and further suppressed self-DNA-induced autoimmunity. However, the implied mechanism remains unclear. Here, extensive molecular dynamics simulations combined with multiple analytical approaches were employed to uncover the allosteric inhibition mechanisms by using the K-to-Q mutations to mimic acetylation. Results suggested that the exterior loops contributed most to the conformational dynamics of cGAS, and two concerted intrinsic motions were observed: the inward/outward or twisting movement for the outer appendage of lobe 1 and the open/closed swing of the active-site loops. Mutations slightly affected the binding of dsDNA and cGAMP. The shift of the conformational sampling of the active-site loops or residues around cGAMP upon mutation might potentially explain the inhibition of cGAS activity. Moreover, the intra- and inter-molecular coupling was weakened upon mutations more or less but via distinct pathways. Hence, conformational dynamics play a vital role in the allosteric inhibition of cGAS upon the studied acetyl-mimic mutations. As the studied acetyl-mimic mutations are located at either the inter-lobe or inter-molecular interfaces, hence except for acetylation, our findings might help the development of new therapeutics against autoimmune diseases due to abnormal cGAS activation by designing inter-lobe or intermolecular allosteric inhibitors.

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC₅₀ values below 10 μM and the most potent one (compound 1) with IC₅₀ of 1.18 ± 0.04 μM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC₅₀ of 0.27 ± 0.02 μM. Then, the structure-activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.

Interplay among Structural Stability, Plasticity, and Energetics Determined by Conformational Attuning of Flexible Loops in PD-1

The dynamics and plasticity of the PD-1/PD-L1 axis are the bottlenecks for the discovery of small-molecule antagonists to perturb this interaction interface significantly. Understanding the process of this protein-protein interaction (PPI) is of fundamental biological interest in structure-based drug designing. Food and Drug Administration (FDA)-approved anti-PD-1 monoclonal antibodies (mAbs) are the first-in-class with distinct binding modes to access this axis clinically; however, their mechanistic aspects remain elusive. Here, we have unveiled the interactive interfaces with PD-L1 and mAbs to investigate the native plasticity of PD-1 at global (structural and dynamical) and local (residue side-chain orientations) levels. We found that the structural stability and coordinated C_α movements are increased in the presence of PD-1's binding partners. The rigorous analysis of these PPIs using computational biophysical approaches revealed PD-1's intrinsic plasticity, its concerted loops' movement (BC, FG, and CC'), distal side-chain motions, and the thermodynamic landscape, which are perturbed remarkably from its unbound to bound states. Based on intra-/inter-residues' contact networks and energetics, the hot-spots have been identified that were found to be essential to arrest the dynamical motions of PD-1 significantly for the rational design of therapeutic agents by mimicking the mAbs mechanism.

Molecular basis of SMAC-XIAP binding and the effect of electrostatic polarization

X-chromosome-linked inhibitor of apoptosis (XIAP) inhibits cell apoptosis. Overexpression of XIAP is widely found in human cancers. Second mitochondria-derived activator of caspase (SMAC) protein inhibits XIAP through binding with Baculovirus Inhibitor of apoptosis protein Repeat (BIR) 3 or BIR2 domain of XIAP. In this study, molecular dynamics (MD) simulations and the alanine scanning calculations by MM-GBSA_IE method were used to investigate the protein-peptide interaction between BIR3 and BIR2 domains of XIAP and SMAC peptide. Energetic contribution of each binding residue is calculated and hotspots on both XIAP and SMAC were identified using computational alanine scanning with interaction entropy method. We found that electrostatic polarization is important in stabilizing the protein-protein complex structure in MD simulation. By using polarized protein-specific charges, much better agreement with experimental result is obtained for calculated binding free energies compared to those using standard (nonpolarizable) AMBER force field. In particular, excellent correlation between calculated binding free energies in alanine scanning with mutational experimental data was obtained for BIR3/SMAC binding.Communicated by Ramaswamy H. Sarma.

Distinct Dynamic and Conformational Features of Human STING in Response to 2'3'-cGAMP and c-di-GMP

The human stimulator of interferon genes protein (hSTING) can bind cyclic dinucleotides (CDNs) to activate the production of type I interferons and inflammatory cytokines. These CDNs can be either bacterial secondary messengers, 3'3'-CDNs, or endogenous 2'3'-cGAMP. cGAMP, with a unique 2'-5' bond, is the most potent activator of hSTING among all CDNs. However, current understanding of the molecular principles underlying the unique ability of 2'3'-cGAMP to potently activate hSTINGs other than 3'3'-CDNs remains incomplete. In this work, molecular dynamics simulations were used to provide an atomistic picture of the binding of 2'3'-cGAMP and one 3'3'-CDN (c-di-GMP) to hSTING. The results suggest that hSTING binds more strongly to 2'3'-cGAMP than to c-di-GMP, which prefers to bind with a more open and flexible state of hSTING. Finally, a potential "dock-lock-anchor" mechanism is proposed for the activation of hSTING upon the binding of a potent ligand. It is believed that deep insights into understanding the binding of hSTING with 3'3'-CDNs and the endogenous 2'3'-cGAMP would help to establish the principles underlying powerful 2'3'-cGAMP signaling and the nature of hSTING activation, as well as related drug design.

Understanding the structural and energetic basis of PD-1 and monoclonal antibodies bound to PD-L1: A molecular modeling perspective

BACKGROUND

The inhibitors blocking the interaction between programmed cell death protein 1(PD-1) and programmed death-ligand 1(PD-L1) can activate the immune response of T cell and eliminate cancer cells. The crystallographic studies have provided structural insights of the interactive interfaces between PD-L1 and its protein ligands. However, the hotspot residues on PD-L1 as well as structural and energetic basis for different protein ligands still need to be further investigated.

METHODS

Molecular modeling methods including molecular dynamics simulation, per-residue free energy decomposition, virtual alanine scanning mutagenesis and residue-residue contact analysis were used to qualitatively and quantitatively analyze the interactions between PD-L1 and different protein ligands.

RESULTS

The results of virtual alanine scanning mutagenesis suggest that Y56, Q66, M115, D122, Y123, R125 are the hotspot residues on PD-L1. The residue-residue contact analysis further shows that PD-1 interacts with PD-L1 mainly by F and G strands while monoclonal antibodies like avelumab and BMS-936559 mainly interact with PD-L1 by CDR2 and CDR3 loops of the heavy chain.

CONCLUSIONS

A structurally similar β-hairpin peptide with 13 or 14 residues was extracted from each protein ligand and these β-hairpin peptides were found tightly binding to the putative hotspot residues on PD-L1.

GENERAL SIGNIFICANCE

This study recognizes the hotspot residues on PD-L1 and uncovers the common structural and energetic basis of different protein ligands binding to PD-L1. These results will be valuable for the design of small molecule or peptide inhibitors targeting on PD-L1.

Trauma in silico: Individual-specific mathematical models and virtual clinical populations

Trauma-induced critical illness is driven by acute inflammation, and elevated systemic interleukin-6 (IL-6) after trauma is a biomarker of adverse outcomes. We constructed a multicompartment, ordinary differential equation model that represents a virtual trauma patient. Individual-specific variants of this model reproduced both systemic inflammation and outcomes of 33 blunt trauma survivors, from which a cohort of 10,000 virtual trauma patients was generated. Model-predicted length of stay in the intensive care unit, degree of multiple organ dysfunction, and IL-6 area under the curve as a function of injury severity were in concordance with the results from a validation cohort of 147 blunt trauma patients. In a subcohort of 98 trauma patients, those with high-IL-6 single-nucleotide polymorphisms (SNPs) exhibited higher plasma IL-6 levels than those with low IL-6 SNPs, matching model predictions. Although IL-6 could drive mortality in individual virtual patients, simulated outcomes in the overall cohort were independent of the propensity to produce IL-6, a prediction verified in the 98-patient subcohort. In silico randomized clinical trials suggested a small survival benefit of IL-6 inhibition, little benefit of IL-1β inhibition, and worse survival after tumor necrosis factor-α inhibition. This study demonstrates the limitations of extrapolating from reductionist mechanisms to outcomes in individuals and populations and demonstrates the use of mechanistic simulation in complex diseases.

Disruption of protein–protein interactions: hot spot detection, structure-based virtual screening and in vitro testing for the anti-cancer drug target – survivin

Hot spot detection at the protein–protein interaction interface using computational tools helped to identify indinavir as survivin inhibitor.

Understanding the molecular basis of EGFR kinase domain/MIG-6 peptide recognition complex using computational analyses

Background

Epidermal growth factor receptor (EGFR) signalling plays a major role in biological processes, including cell proliferation, differentiation and survival. Since the over-expression of EGFR causes human cancers, EGFR is an attractive drug target. A tumor suppressor endogenous protein, MIG-6, is known to suppress EGFR over-expression by binding to the C-lobe of EGFR kinase. Thus, this C-lobe of the EGFR kinase is a potential new target for EGFR kinase activity inhibition. In this study, molecular dynamics (MD) simulations and binding free energy calculations were used to investigate the protein-peptide interactions between EGFR kinase and a 27-residue peptide derived from MIG-6_s1 segment (residues 336–362).

Results

These 27 residues of MIG-6_s1 were modeled from the published MIG-6 X-ray structure. The binding dynamics were detailed by applying the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method to predict the binding free energy. Both van der Waals interactions and non-polar solvation were favorable driving forces for binding process. Six residues of EGFR kinase and eight residues of MIG-6_s1 residues were shown to be responsible for interface binding in which we investigated per residue free energy decomposition and the results from the computational alanine scanning approach. These residues also had higher hydrogen bond occupancies than other residues at the binding interface. The results from the aforementioned calculations reasonably agreed with the previous experimental mutagenesis studies.

Conclusions

Molecular dynamics simulations were used to investigate the interactions of MIG-6_s1 to EGFR kinase domain. Our study provides an insight into such interactions that is useful in guiding the design of novel anticancer therapeutics. The information on our modelled peptide interface with EGFR kinase could be a possible candidate for an EGFR dimerization inhibitor.

Hitting the right spot: Mechanism of action of OPB-31121, a novel and potent inhibitor of the Signal Transducer and Activator of Transcription 3 (STAT3)

STAT3 is a key element in many oncogenic pathways and, like other transcription factors, is an attractive target for development of novel anticancer drugs. However, interfering with STAT3 functions has been a difficult task and very few small molecule inhibitors have made their way to the clinic. OPB-31121, an anticancer compound currently in clinical trials, has been reported to affect STAT3 signaling, although its mechanism of action has not been unequivocally demonstrated. In this study, we used a combined computational and experimental approach to investigate the molecular target and the mode of interaction of OPB-31121 with STAT3. In parallel, similar studies were performed with known STAT3 inhibitors (STAT3i) to validate our approach. Computational docking and molecular dynamics simulation (MDS) showed that OPB-31121 interacted with high affinity with the SH2 domain of STAT3. Interestingly, there was no overlap of the OPB-31121 binding site with those of the other STAT3i. Computational predictions were confirmed by in vitro binding assays and competition experiments along with site-directed mutagenesis of critical residues in the STAT3 SH2 domain. Isothermal titration calorimetry experiments demonstrated the remarkably high affinity of OPB-31121 for STAT3 with Kd (10 nM) 2-3 orders lower than other STAT3i. Notably, a similar ranking of the potency of the compounds was observed in terms of inhibition of STAT3 phosphorylation, cancer cell proliferation and clonogenicity. These results suggest that the high affinity and efficacy of OPB-31121 might be related to the unique features and mode of interaction of OPB-31121 with STAT3. These unique characteristics make OPB-31121 a promising candidate for further development and an interesting lead for designing new, more effective STAT3i.

Predicting the mixture effects of three pesticides by integrating molecular simulation with concentration addition modeling

Molecular simulation techniques are used to identify the mode of inhibition of chemicals at the ligand–receptor level.

Structure-Based, Rational Design of T Cell Receptors

Adoptive cell transfer using engineered T cells is emerging as a promising treatment for metastatic melanoma. Such an approach allows one to introduce T cell receptor (TCR) modifications that, while maintaining the specificity for the targeted antigen, can enhance the binding and kinetic parameters for the interaction with peptides (p) bound to major histocompatibility complexes (MHC). Using the well-characterized 2C TCR/SIYR/H-2K(b) structure as a model system, we demonstrated that a binding free energy decomposition based on the MM-GBSA approach provides a detailed and reliable description of the TCR/pMHC interactions at the structural and thermodynamic levels. Starting from this result, we developed a new structure-based approach, to rationally design new TCR sequences, and applied it to the BC1 TCR targeting the HLA-A2 restricted NY-ESO-1_157–165 cancer-testis epitope. Fifty-four percent of the designed sequence replacements exhibited improved pMHC binding as compared to the native TCR, with up to 150-fold increase in affinity, while preserving specificity. Genetically engineered CD8⁺ T cells expressing these modified TCRs showed an improved functional activity compared to those expressing BC1 TCR. We measured maximum levels of activities for TCRs within the upper limit of natural affinity, K_D = ∼1 − 5 μM. Beyond the affinity threshold at K_D < 1 μM we observed an attenuation in cellular function, in line with the “half-life” model of T cell activation. Our computer-aided protein-engineering approach requires the 3D-structure of the TCR-pMHC complex of interest, which can be obtained from X-ray crystallography. We have also developed a homology modeling-based approach, TCRep 3D, to obtain accurate structural models of any TCR-pMHC complexes when experimental data is not available. Since the accuracy of the models depends on the prediction of the TCR orientation over pMHC, we have complemented the approach with a simplified rigid method to predict this orientation and successfully assessed it using all non-redundant TCR-pMHC crystal structures available. These methods potentially extend the use of our TCR engineering method to entire TCR repertoires for which no X-ray structure is available. We have also performed a steered molecular dynamics study of the unbinding of the TCR-pMHC complex to get a better understanding of how TCRs interact with pMHCs. This entire rational TCR design pipeline is now being used to produce rationally optimized TCRs for adoptive cell therapies of stage IV melanoma.