Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery

GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery pipeline. With the great power of AI, it is possible to rapidly search the vast chemical space for potential drug-target interactions (DTIs) between candidate drug molecules and disease protein targets. However, only a small proportion of molecules have labelled DTIs, consequently limiting the performance of AI-based drug screening. To solve this problem, a machine learning-based approach with great ability to generalize DTI prediction across molecules is desirable. Many existing machine learning approaches for DTI identification failed to exploit the full information with respect to the topological structures of candidate molecules. To develop a better approach for DTI prediction, we propose GraphormerDTI, which employs the powerful Graph Transformer neural network to model molecular structures. GraphormerDTI embeds molecular graphs into vector-format representations through iterative Transformer-based message passing, which encodes molecules' structural characteristics by node centrality encoding, node spatial encoding and edge encoding. With a strong structural inductive bias, the proposed GraphormerDTI approach can effectively infer informative representations for out-of-sample molecules and as such, it is capable of predicting DTIs across molecules with an exceptional performance. GraphormerDTI integrates the Graph Transformer neural network with a 1-dimensional Convolutional Neural Network (1D-CNN) to extract the drugs' and target proteins' representations and leverages an attention mechanism to model the interactions between them. To examine GraphormerDTI's performance for DTI prediction, we conduct experiments on three benchmark datasets, where GraphormerDTI achieves a superior performance than five state-of-the-art baselines for out-of-molecule DTI prediction, including GNN-CPI, GNN-PT, DeepEmbedding-DTI, MolTrans and HyperAttentionDTI, and is on a par with the best baseline for transductive DTI prediction. The source codes and datasets are publicly accessible at https://github.com/mengmeng34/GraphormerDTI.

Assessing the potential of NS2B/NS3 protease inhibitors biomarker in curbing dengue virus infections: In silico vs. In vitro approach

An increase in the occurrence of viral infectious diseases is a global concern for human health. According to a WHO report, dengue virus (DENV) is one of the most common viral diseases affecting approximately 400 million people annually, with worsening symptoms in nearly 1% of cases. Both academic and industrial researchers have conducted numerous studies on viral epidemiology, virus structure and function, source and route of infection, treatment targets, vaccines, and drugs. The development of CYD-TDV or Dengvaxia^® vaccine has been a major milestone in dengue treatment. However, evidence has shown that vaccines have some drawbacks and limitations. Therefore, researchers are developing dengue antivirals to curb infections. DENV NS2B/NS3 protease is a DENV enzyme essential for replication and virus assembly, making it an interesting antiviral target. For faster hit and lead recognition of DENV targets, methods to screen large number of molecules at lower costs are essential. Similarly, an integrated and multidisciplinary approach involving in silico screening and confirmation of biological activity is required. In this review, we discuss recent strategies for searching for novel DENV NS2B/NS3 protease inhibitors from the in silico and in vitro perspectives, either by applying one of the approaches or by integrating both. Therefore, we hope that our review will encourage researchers to integrate the best strategies and encourage further developments in this area.

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

Fragment tailoring strategy to design novel chemical entities as potential binders of novel corona virus main protease

The recent pandemic of severe acute respiratory syndrome-coronavirus2 (SARS-CoV-2) infection (COVID-19) has put the world on serious alert. The main protease of SARS-CoV-2 (SARS-CoV-2-MPro) cleaves the long polyprotein chains to release functional proteins required for replication of the virus and thus is a potential drug target to design new chemical entities in order to inhibit the viral replication in human cells. The current study employs state of art computational methods to design novel molecules by linking molecular fragments which specifically bind to different constituent sub-pockets of the SARS-CoV-2-MPro binding site. A huge library of 191678 fragments was screened against the binding cavity of SARS-CoV-2-MPro and high affinity fragments binding to adjacent sub-pockets were tailored to generate new molecules. These newly formed molecules were further subjected to molecular docking, ADMET filters and MM-GBSA binding energy calculations to select 17 best molecules (named as MP-In1 to MP-In17), which showed comparable binding affinities and interactions with the key binding site residues as the reference ligand. The complexes of these 17 molecules and the reference molecule with SARS-CoV-2-MPro, were subjected to molecular dynamics simulations, which assessed the stabilities of their binding with SARS-CoV-2-MPro. Fifteen molecules were found to form stable complexes with SARS-CoV-2-MPro. These novel chemical entities designed specifically according to the pharmacophoric requirements of SARS-CoV-2-MPro binding pockets showed good synthetic feasibility and returned no exact match when searched against chemical databases. Considering their interactions, binding efficiencies and novel chemotypes, they can be further evaluated as potential starting points for SARS-CoV-2 drug discovery.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Association of variants in MYH7, MYBPC3 and TNNT2 with sudden cardiac death-related risk factors in Brazilian patients with hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is characterized by unexplained left ventricular hypertrophy (LVH) and is one of the major causes of sudden cardiac death (SCD). An exon-targeted gene sequencing strategy was used to investigate the association of functional variants in sarcomeric genes (MYBPC3, MYH7 and TNNT2) with severe LVH and other SCD-related risk factors in Brazilian HCM patients. Clinical data of 55 HCM patients attending a Cardiology Hospital (Sao Paulo city, Brazil) were recorded. Severe LVH, aborted SCD, family history of SCD, syncope, non-sustained ventricular tachycardia and abnormal blood pressure in response to exercise were evaluated as SCD risk factors. Blood samples were obtained for genomic DNA extraction and the exons and untranslated regions of the MYH7, MYBPC3 and TNNT2 were sequenced using Nextera® and MiSEq® reagents. Variants were identified and annotated using in silico tools, and further classified as pathogenic or benign according to the American College of Medical Genetics and Genomics guidelines. Variants with functional effects were identified in MYBPC3 (n = 9), MYH7 (n = 6) and TNNT2 (n = 4). The benign variants MYBPC3 p.Val158Met and TNNT2 p.Lys263Arg were associated with severe LVH (p < 0.05), and the MYH7 p.Val320Met (pathogenic) was associated with family history of SCD (p = 0.037). Increased risk for severe LVH was found in carriers of MYBPC3 Met158 (c.472 A allele, OR = 13.5, 95% CI = 1.80-101.12, p = 0.011) or combined variants (MYBPC3, MYH7 and TNNT2: OR = 12.39, 95% CI = 2.14-60.39, p = 0.004). Carriers of TNNT2 p.Lys263Arg and combined variants had higher values of septum thickness than non-carriers (p < 0.05). Molecular modeling analysis showed that MYBPC3 158Met reduces the interaction of cardiac myosin-binding protein C (cMyBP-C) RASK domain (amino acids Arg215-Ala216-Ser217-Lys218) with tropomyosin. In conclusion, the variants MYBPC3 p.Val158Met, TNNT2 p.Lys263Arg and MYH7 p.Val320Met individually or combined contribute to the risk of sudden cardiac death and other outcomes of hypertrophic cardiomyopathy.