Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

PLAGCA: Predicting protein-ligand binding affinity with the graph cross-attention mechanism

Accurate prediction of protein-ligand binding affinity plays a crucial role in drug discovery. However, determining the binding affinity of protein-ligands through biological experimental approaches is both time-consuming and expensive. Although some computational methods have been developed to predict protein-ligands binding affinity, most existing methods extract the global features of proteins and ligands through separate encoders, without considering to extract the local pocket interaction features of protein-ligand complexes, resulting in the limited prediction accuracy. In this work, we proposed a novel Protein-Ligand binding Affinity prediction method (named PLAGCA) by introducing Graph Cross-Attention mechanism to learn the local three-dimensional (3D) features of protein-ligand pockets, and integrating the global sequence/string features and local graph interaction features of protein-ligand complexes. PLAGCA uses sequence encoding and self-attention to extract the protein/ligand global features from protein FASTA sequences/ligand SMILES strings, adopts graph neural network and cross-attention to extract the protein-ligand local interaction features from the molecular structures of protein binding pockets and ligands. All these features are concatenated and input into a multi-layer perceptron (MLP) for predicting the protein-ligand binding affinity. The experimental results show that our PLAGCA outperforms other state-of-the-art computational methods, and it can effectively predict protein-ligand binding affinity with superior generalization capability. PLAGCA can capture the critical functional residues that are important contribution to the protein-ligand binding.

PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model

Deep learning methods have played an increasingly pivotal role in advancing side-chain packing and mutation effect prediction (ΔΔG) for protein complexes. Although these two tasks are inherently closely related, they are typically treated separately in practice. Furthermore, the lack of effective post-processing in most approaches results in sub-optimal refinement of generated conformations, limiting the plausibility of the predicted conformations. In this study, we introduce an integrated framework, PackPPI, which employs a diffusion model and a proximal optimization algorithm to improve side-chain prediction for protein complexes while using learned representations to predict ΔΔG. The results demonstrate that PackPPI achieved the lowest atom RMSD (0.9822) on the CASP15 dataset. The proximal optimization algorithm effectively reduces spatial clashes between side-chain atoms while maintaining a low-energy landscape. Furthermore, PackPPI achieves state-of-the-art performance in predicting binding affinity changes induced by multi-point mutations on the SKEMPI v2.0 dataset. These findings underscore the potential of PackPPI as a robust and versatile computational tool for protein design and engineering. The implementation of PackPPI is available at https://github.com/Jackz915/PackPPI.

Edge-enhanced interaction graph network for protein-ligand binding affinity prediction

Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly effective in modeling spatial relationships and three-dimensional structures within intermolecular. In this paper, we introduce a graph neural network-based model named EIGN to predict protein-ligand binding affinity. The model consists of three main components: the normalized adaptive encoder, the molecular information propagation module, and the output module. Experimental results indicate that EIGN achieves root mean squared error of 1.126 and Pearson correlation coefficient of 0.861 on CASF-2016. Additionally, our model outperforms state-of-the-art methods on CASF-2013, CASF-2016, and the CSAR-NRC set, showing exceptional accuracy and robust generalization ability. To further validate the effectiveness of EIGN, we conducted several experiments, including ablation studies, feature importance analysis, data similarity analysis, and others, to evaluate its performance and applicability.

Evaluation of Structure Prediction and Molecular Docking Tools for Therapeutic Peptides in Clinical Use and Trials Targeting Coronary Artery Disease

This study evaluates the performance of various structure prediction tools and molecular docking platforms for therapeutic peptides targeting coronary artery disease (CAD). Structure prediction tools, including AlphaFold 3, I-TASSER 5.1, and PEP-FOLD 4, were employed to generate accurate peptide conformations. These methods, ranging from deep-learning-based (AlphaFold) to template-based (I-TASSER 5.1) and fragment-based (PEP-FOLD), were selected for their proven capabilities in predicting reliable structures. Molecular docking was conducted using four platforms (HADDOCK 2.4, HPEPDOCK 2.0, ClusPro 2.0, and HawDock 2.0) to assess binding affinities and interactions. A 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability of the peptide-receptor complexes, along with Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations to determine binding free energies. The results demonstrated that Apelin, a therapeutic peptide, exhibited superior binding affinities and stability across all platforms, making it a promising candidate for CAD therapy. Apelin's interactions with key receptors involved in cardiovascular health were notably stronger and more stable compared to the other peptides tested. These findings underscore the importance of integrating advanced computational tools for peptide design and evaluation, offering valuable insights for future therapeutic applications in CAD. Future work should focus on in vivo validation and combination therapies to fully explore the clinical potential of these therapeutic peptides.

Anilino-1,4-naphthoquinones as potent mushroom tyrosinase inhibitors: in vitro and in silico studies

Tyrosinase, a pivotal enzyme in melanin synthesis, is a primary target for the development of depigmenting agents. In this work, in vitro and in silico techniques were employed to identify novel tyrosinase inhibitors from a set of 12 anilino-1,4-naphthoquinone derivatives. Results from the mushroom tyrosinase activity assay indicated that, among the 12 derivatives, three compounds (1, 5, and 10) demonstrated the most significant inhibitory activity against mushroom tyrosinase, surpassing the effectiveness of the kojic acid. Molecular docking revealed that all studied derivatives interacted with copper ions and amino acid residues at the enzyme active site. Molecular dynamics simulations provided insights into the stability of enzyme-inhibitor complexes, in which compounds 1, 5, and particularly 10 displayed greater stability, atomic contacts, and structural compactness than kojic acid. Drug likeness prediction further strengthens the potential of anilino-1,4-naphthoquinones as promising candidates for the development of novel tyrosinase inhibitors for the treatment of hyperpigmentation disorders.

Ensembling methods for protein-ligand binding affinity prediction

Protein-ligand binding affinity prediction is a key element of computer-aided drug discovery. Most of the existing deep learning methods for protein-ligand binding affinity prediction utilize single models and suffer from low accuracy and generalization capability. In this paper, we train 13 deep learning models from combinations of 5 input features. Then, we explore all possible ensembles of the trained models to find the best ensembles. Our deep learning models use cross-attention and self-attention layers to extract short and long-range interactions. Our method is named Ensemble Binding Affinity (EBA). EBA extracts information from various models using different combinations of input features, such as simple 1D sequential and structural features of the protein-ligand complexes rather than 3D complex features. EBA is implemented to accurately predict the binding affinity of a protein-ligand complex. One of our ensembles achieves the highest Pearson correlation coefficient (R) value of 0.914 and the lowest root mean square error (RMSE) value of 0.957 on the well-known benchmark test set CASF2016. Our ensembles show significant improvements of more than 15% in R-value and 19% in RMSE on both well-known benchmark CSAR-HiQ test sets over the second-best predictor named CAPLA. Furthermore, the superior performance of the ensembles across all metrics compared to existing state-of-the-art protein-ligand binding affinity prediction methods on all five benchmark test datasets demonstrates the effectiveness and robustness of our approach. Therefore, our approach to improving binding affinity prediction between proteins and ligands can contribute to improving the success rate of potential drugs and accelerate the drug development process.

Application of Silk Light Chain on a Graphene Composite Surface: A Molecular Dynamics and Electrochemical Experiment

The surface functionalization of pristine graphene (PG) with beneficial biocomposites is important for biomedical and tissue engineering. This study introduces silk light chain as novel biocomposites to increase the biocompatibility of PG. We explored the supramolecular structures of the silk heavy and light chains. Through molecular dynamics, we compared and analyzed the structural effects and binding mechanisms of these domains in their interaction with PG. Our results highlighted a significant hydrophobic interaction between the silk light chain and PG, without structural collapse. The supramolecular structure of the silk light chain was identified by analyzing the amino acids bound to PG. Moreover, using the silk light chain, the hydrophobic surface of PG has changed to a hydrophilic surface, and the silk light-chain-PG electron transfer rate was evaluated for the graphene congeners: graphene oxide (GO) and reduced graphene oxide. Therefore, we are confident that the dispersibility and biocompatibility of PG can be increased using silk light chains, which will contribute to broadening the field of application of PG-based materials.

Catalytic mechanism and kinetics of malate dehydrogenase

Malate dehydrogenase (MDH) is a ubiquitous and central enzyme in cellular metabolism, found in all kingdoms of life, where it plays vital roles in the cytoplasm and various organelles. It catalyzes the reversible NAD+-dependent reduction of L-malate to oxaloacetate. This review describes the reaction mechanism for MDH and the effects of mutations in and around the active site on catalytic activity and substrate specificity, with a particular focus on the loop that encloses the active site after the substrates have bound. While MDH exhibits selectivity for its preferred substrates, mutations can alter the specificity of MDH for each cosubstrate. The kinetic characteristics and similarities of a variety of MDH isozymes are summarized, and they illustrate that the KM values are consistent with the relative concentrations of the substrates in cells. As a result of its existence in different cellular environments, MDH properties vary, making it an attractive model enzyme for studying enzyme activity and structure under different conditions.

Genetic features of patients with MPS type IIIB: Description of five pathogenic gene variations

BACKGROUND

There are four distinct forms of Sanfilippo syndrome (MPS type III), each of which is an autosomal lysosomal storage disorder. These forms are caused by abnormalities in one of four lysosomal enzymes. This study aimed to identify possible genetic variants that contribute to Sanfilippo IIIB in 14 independent families in Southwest Iran.

METHODS

Patients were included if their clinical features and enzyme assay results were suggestive. The patients were subsequently subjected to Sanger Sequencing to screen for Sanfilippo-related genes. Additional investigations have been conducted using various computational analyses to determine the probable functional effects of diagnosed variants.

RESULTS

Five distinct variations were identified in the NAGLU gene. This included two novel variants in two distinct families and three previously reported variants in 12 distinct families. All of these variations were recognized as pathogenic using the MutationTaster web server. In silico analysis showed that all detected variants affected protein structural stability; four destabilized protein structures, and the fifth variation had the opposite effect.

CONCLUSION

In this study, two novel variations in the NAGLU gene were identified. The results of this study positively contribute to the mutation diversity of the NAGLU gene. To identify new disease biomarkers and therapeutic targets, precision medicine must precisely characterize and account for genetic variations. New harmful gene variants are valuable for updating gene databases concerning Sanfilippo disease variations and NGS gene panels. This may also improve genetic counselling for rapid risk examinations and disease surveillance.

Structural investigation of Trypanosoma cruzi Akt-like kinase as drug target against Chagas disease

According to the World Health Organization, Chagas disease (CD) is the most prevalent poverty-promoting neglected tropical disease. Alarmingly, climate change is accelerating the geographical spreading of CD causative parasite, Trypanosoma cruzi, which additionally increases infection rates. Still, CD treatment remains challenging due to a lack of safe and efficient drugs. In this work, we analyze the viability of T. cruzi Akt-like kinase (TcAkt) as drug target against CD including primary structural and functional information about a parasitic Akt protein. Nuclear Magnetic Resonance derived information in combination with Molecular Dynamics simulations offer detailed insights into structural properties of the pleckstrin homology (PH) domain of TcAkt and its binding to phosphatidylinositol phosphate ligands (PIP). Experimental data combined with Alpha Fold proposes a model for the mechanism of action of TcAkt involving a PIP-induced disruption of the intramolecular interface between the kinase and the PH domain resulting in an open conformation enabling TcAkt kinase activity. Further docking experiments reveal that TcAkt is recognized by human inhibitors PIT-1 and capivasertib, and TcAkt inhibition by UBMC-4 and UBMC-6 is achieved via binding to TcAkt kinase domain. Our in-depth structural analysis of TcAkt reveals potential sites for drug development against CD, located at activity essential regions.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Allelic heterogeneity and abnormal vesicle recycling in PLAA-related neurodevelopmental disorders

The human PLAA gene encodes Phospholipase-A2-Activating-Protein (PLAA) involved in trafficking of membrane proteins. Through its PUL domain (PLAP, Ufd3p, and Lub1p), PLAA interacts with p97/VCP modulating synaptic vesicles recycling. Although few families carrying biallelic PLAA variants were reported with progressive neurodegeneration, consequences of monoallelic PLAA variants have not been elucidated. Using exome or genome sequencing we identified PLAA de-novo missense variants, affecting conserved residues within the PUL domain, in children affected with neurodevelopmental disorders (NDDs), including psychomotor regression, intellectual disability (ID) and autism spectrum disorders (ASDs). Computational and in-vitro studies of the identified variants revealed abnormal chain arrangements at C-terminal and reduced PLAA-p97/VCP interaction, respectively. These findings expand both allelic and phenotypic heterogeneity associated to PLAA-related neurological disorders, highlighting perturbed vesicle recycling as a potential disease mechanism in NDDs due to genetic defects of PLAA.

Engineering of human tryptophan hydroxylase 2 for efficient synthesis of 5-hydroxytryptophan

L-Tryptophan hydroxylation catalyzed by tryptophan hydroxylase (TPH) presents a promising method for synthesizing 5-hydroxytryptophan (5-HTP), yet the limited activity of wild-type human TPH2 restricts its application. A high-activity mutant, MT10 (H318E/H323E), was developed through semi-rational active site saturation testing (CAST) of wild-type TPH2, exhibiting a 2.85-fold increase in kcat/Km over the wild type, thus enhancing catalytic efficiency. Two biotransformation systems were developed, including an in vitro one-pot system and a Whole-Cell Catalysis System (WCCS). In the WCCS, MT10 achieved a conversion rate of only 31.5 % within 32 h. In the one-pot reaction, MT10 converted 50 mM L-tryptophan to 44.5 mM 5-HTP within 8 h, achieving an 89 % conversion rate, outperforming the M1 (NΔ143/CΔ26) variant. Molecular dynamics simulations indicated enhanced interactions of MT10 with the substrate, suggesting improved binding affinity and system stability. This study offers an effective approach for the efficient production of 5-HTP.

Computational approaches to investigate fluoride binding, selectivity and transport across the membrane

The use of molecular dynamics (MD) simulations to study biomolecular systems has proven reliable in elucidating atomic-level details of structure and function. In this chapter, MD simulations were used to uncover new insights into two phylogenetically unrelated bacterial fluoride (F-) exporters: the CLCF F-/H+ antiporter and the Fluc F- channel. The CLCF antiporter, a member of the broader CLC family, has previously revealed unique stoichiometry, anion-coordinating residues, and the absence of an internal glutamate crucial for proton import in the CLCs. Through MD simulations enhanced with umbrella sampling, we provide insights into the energetics and mechanism of the CLCF transport process, including its selectivity for F- over HF. In contrast, the Fluc F- channel presents a novel architecture as a dual topology dimer, featuring two pores for F- export and a central non-transported sodium ion. Using computational electrophysiology, we simulate the electrochemical gradient necessary for F- export in Fluc and reveal details about the coordination and hydration of both F- and the central sodium ion. The procedures described here delineate the specifics of these advanced techniques and can also be adapted to investigate other membrane protein systems.

Delayed Graft Function in Pediatric Kidney Transplant: Risk Factors and Outcomes

OBJECTIVES

We aimed to identify risk factors and outcomes of delayed graft function in pediatric kidney transplant.

MATERIALS AND METHODS

This retrospective study included all kidney transplant recipients ≤19 years old followed up in our department for a period of 34 years, from January 1989 to December 2022.

RESULTS

We included 113 kidney transplant recipients. Delayed graft function occurred in 17 cases (15%). Posttransplant red blood cell transfusion was strongly associated with delayed graft function (adjusted odds ratio = 23.91; 95% CI, 2.889-197.915). Use of allografts with multiple arteries and cold ischemia time >20 hours were risk factors for delayed graft function (adjusted odds ratio = 52.51 and 49.4; 95% CI, 2.576-1070.407 and 1.833-1334.204, respectively). Sex-matched transplants and living donors were protective factors for delayed graft function (adjusted odds ratio = 0.043 and 0.027; 95% CI, 0.005-0.344 and 0.003-0.247, respectively). Total HLA mismatches <3 played a protective role for delayed graft function (adjusted odds ratio = 0.114; 95% CI, 0.020-0.662), whereas transplant within compatible but different blood types increased the risk of delayed graft function (adjusted odds ratio = 20.54; 95% CI, 1.960- 215.263). No significant correlation was shown between delayed graft function and allograft survival (P = .190). Our study suggested delayed graft function as a key factor in allograft rejection-free survival (adjusted odds ratio = 3.832; 95% CI, 1.186-12.377). Delayed graft function was a negative factor for early graft function; patients with delayed graft function had a lower estimated glomerular filtration rate at discharge (P = .024) and at 3 (P = .034), 6 (P = .019), and 12 months (P = .011) posttransplant.

CONCLUSIONS

Delayed graft function is a major determinant of early graft function and allograft rejection-free survival. Further research is required to establish proper preventive measures.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

In Silico and In Vivo Evaluation of the Maqui Berry (Aristotelia chilensis (Mol.) Stuntz) on Biochemical Parameters and Oxidative Stress Markers in a Metabolic Syndrome Model

Metabolic syndrome (MetS) is a complex disease that includes metabolic and physiological alterations in various organs such as the heart, pancreas, liver, and brain. Reports indicate that blackberry consumption, such as maqui berry, has a beneficial effect on chronic diseases such as cardiovascular disease, obesity, and diabetes. In the present study, in vivo and in silico studies have been performed to evaluate the molecular mechanisms implied to improve the metabolic parameters of MetS. Fourteen-day administration of maqui berry reduces weight gain, blood fasting glucose, total blood cholesterol, triacylglycerides, insulin resistance, and blood pressure impairment in the diet-induced MetS model in male and female rats. In addition, in the serum of male and female rats, the administration of maqui berry (MB) improved the concentration of MDA, the activity of SOD, and the formation of carbonyls in the group subjected to the diet-induced MetS model. In silico studies revealed that delphinidin and its glycosylated derivatives could be ligands of some metabolic targets such as α-glucosidase, PPAR-α, and PPAR-γ, which are related to MetS parameters. The experimental results obtained in the study suggest that even at low systemic concentrations, anthocyanin glycosides and aglycones could simultaneously act on different targets related to MetS. Therefore, these molecules could be used as coadjuvants in pharmacological interventions or as templates for designing new multitarget molecules to manage patients with MetS.

Attention-based solubility prediction of polysulfide and electrolyte analysis for lithium-sulfur batteries

During the continuous charge and discharge process in lithium-sulfur batteries, one of the next-generation batteries, polysulfides are generated in the battery's electrolyte, and impact its performance in terms of power and capacity by involving the process. The amount of polysulfides in the electrolyte could be estimated by the change of the Gibbs free energy of the electrolyte, [Formula: see text] in the presence of polysulfide. However, obtaining [Formula: see text] of the diverse mixtures of components in the electrolyte is a complex and expensive task that shows itself as a bottleneck in optimization of electrolytes. In this work, we present a machine-learning approach for predicting [Formula: see text] of electrolytes. The proposed architecture utilizes (1) an attention-based model (Attentive FP), a contrastive learning model (MolCLR) or morgan fingerprints to represent chemical components, and (2) transformers to account for the interactions between chemicals in the electrolyte. This architecture was not only capable of predicting electrolyte properties, including those of chemicals not used during training, but also providing insights into chemical interactions within electrolytes. It revealed that interactions with other chemicals relate to the logP and molecular weight of the chemicals.

Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.

In silico resources help combat cancer drug resistance mediated by target mutations

Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.

3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive models. State-of-the-art protein descriptors, however, are calculated from the protein sequence and neglect the dynamic nature of proteins. This dynamic nature can be computationally simulated with molecular dynamics (MD). Here, novel 3D dynamic protein descriptors (3DDPDs) were designed to be applied in bioactivity prediction tasks with PCM models. As a test case, publicly available G protein-coupled receptor (GPCR) MD data from GPCRmd was used. GPCRs are membrane-bound proteins, which are activated by hormones and neurotransmitters, and constitute an important target family for drug discovery. GPCRs exist in different conformational states that allow the transmission of diverse signals and that can be modified by ligand interactions, among other factors. To translate the MD-encoded protein dynamics two types of 3DDPDs were considered: one-hot encoded residue-specific (rs) and embedding-like protein-specific (ps) 3DDPDs. The descriptors were developed by calculating distributions of trajectory coordinates and partial charges, applying dimensionality reduction, and subsequently condensing them into vectors per residue or protein, respectively. 3DDPDs were benchmarked on several PCM tasks against state-of-the-art non-dynamic protein descriptors. Our rs- and ps3DDPDs outperformed non-dynamic descriptors in regression tasks using a temporal split and showed comparable performance with a random split and in all classification tasks. Combinations of non-dynamic descriptors with 3DDPDs did not result in increased performance. Finally, the power of 3DDPDs to capture dynamic fluctuations in mutant GPCRs was explored. The results presented here show the potential of including protein dynamic information on machine learning tasks, specifically bioactivity prediction, and open opportunities for applications in drug discovery, including oncology.

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. We illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification.

Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning

Catalysis and fidelity of multisubunit RNA polymerases rely on a highly conserved active site domain called the trigger loop (TL), which achieves roles in transcription through conformational changes and interaction with NTP substrates. The mutations of TL residues cause distinct effects on catalysis including hypo- and hyperactivity and altered fidelity. We applied molecular dynamics simulation (MD) and machine learning (ML) techniques to characterize TL mutations in the Saccharomyces cerevisiae RNA Polymerase II (Pol II) system. We did so to determine relationships between individual mutations and phenotypes and to associate phenotypes with MD simulated structural alterations. Using fitness values of mutants under various stress conditions, we modeled phenotypes along a spectrum of continual values. We found that ML could predict the phenotypes with 0.68 R2 correlation from amino acid sequences alone. It was more difficult to incorporate MD data to improve predictions from machine learning, presumably because MD data is too noisy and possibly incomplete to directly infer functional phenotypes. However, a variational auto-encoder model based on the MD data allowed the clustering of mutants with different phenotypes based on structural details. Overall, we found that a subset of loss-of-function (LOF) and lethal mutations tended to increase distances of TL residues to the NTP substrate, while another subset of LOF and lethal substitutions tended to confer an increase in distances between TL and bridge helix (BH). In contrast, some of the gain-of-function (GOF) mutants appear to cause disruption of hydrophobic contacts among TL and nearby helices.

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism

MOTIVATION

Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.

RESULTS

In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications.

AVAILABILITY AND IMPLEMENTATION

The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities.

Structural basis for the mutation-induced dysfunction of the human IL-15/IL-15α receptor complex

In silico strategies offer a reliable, fast, and inexpensive, way compared to the clumsy in vitro approaches to boost understanding of the effect of amino acid substitution on the structure and consequently the associated function of proteins. In the present work, we report an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the Interleukin-15 (IL-15) protein and its receptor Interleukin-15α (IL-15α), consequently, providing qualitative and quantitative details of the key molecular determinants in ligand/receptor recognition. Molecular dynamics simulations were used to investigate the dynamic behavior of the specific binding between IL-15 and IL-15α followed by estimation of the free energies via molecular mechanics/generalized Born surface area (MM/GBSA). In particular, residues Y26, E46, E53, and E89 of the IL-15 protein receptor-binding domain are identified as main hot spots, shaping and governing the stability of the assembly. These results can be used for the development of neutralizing antibodies and the effective structure-based design of protein-protein interaction inhibitors against the so-called orphan disease, vitiligo.

Selective Non-toxics Inhibitors Targeting DHFR for Tuberculosis and Cancer Therapy: Pharmacophore Generation and Molecular Dynamics Simulation

Dihydrofolate reductase (DHFR) is a crucial enzyme that catalyzes the conversion of folic acid. Its reserved properties and significance in both human (h-DHFR) and mycobacterium (mt-DHFR) make it a challenging target for developing drugs against cancer and bacterial infections. Although methotrexate (MTX) is commonly used for cancer therapy and bacterial infections, it has a toxic profile. In this study, we aimed to identify selective and non-toxic inhibitors against h-DHFR and mt-DHFR using an in silico approach. From a data set of 8 412 inhibitors, 11 compounds passed the toxicity and drug-likeness tests, and their interaction with h-DHFR and mt-DHFR was studied by performing molecular docking. To evaluate the inhibitory activity of the compounds against mt-DHFR, five known reference ligands and the natural ligand (dihydrofolate) were used to generate a pharmacophoric map. Two potential selective inhibitors for mt-DHFR and h-DHFR were selected for further investigation using molecular dynamics for 100 ns. As a result, BDBM18226 was identified as the best compound selective for mt-DHFR, non-toxic, with five features listed in the map, with a binding energy of -9.6 kcal/mol. BDBM50145798 was identified as a non-toxic selective compound with a better affinity than MTX for h-DHFR. Molecular dynamics of the two best ligands suggest that they provide more stable, compact, and hydrogen bond interactions with the protein. Our findings could significantly expand the chemical space for new mt-DHFR inhibitors and provide a non-toxic alternative toward h-DHFR for the respective treatment of tuberculosis and cancer therapy.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

Discovering potential inhibitors against SARS-CoV-2 by targeting Nsp13 Helicase

The rise in the incidence of COVID-19 as a result of SARS-CoV-2 infection has threatened public health globally. Till now, there have been no proper prophylactics available to fight COVID-19, necessitating the advancement and evolution of effective curative against SARS-CoV-2. This study aimed at the nonstructural protein 13 (nsp13) helicase as a promising target for drug development against COVID-19. A unique collection of nucleoside analogs was screened against the SARS-CoV-2 helicase protein, for which a molecular docking experiment was executed to depict the selected ligand's binding affinity with the SARS-CoV-2 helicase proteins. Simultaneously, molecular dynamic simulations were performed to examine the protein's binding site's conformational stability, flexibility, and interaction with the ligands. Key nucleoside ligands were selected for pharmacokinetic analysis based on their docking scores. Selected ligands (cordycepin and pritelivir) showed excellent pharmacokinetics and were well stabilized at the proteins' binding site throughout the MD simulation. We have also performed binding free energy analysis or the binding characteristics of ligands with Nsp13 by using MM-PBSA and MM-GBSA. Free energy calculation by MM-PBSA and MM-GBSA analysis suggests that pritelivir may work as viable therapeutics for efficient drug advancement against SARS-CoV-2 Nsp13 helicase, potentially arresting the SARS-CoV-2 replication.Communicated by Ramaswamy H. Sarma.

Mutation-Specific Differences in Kv7.1 (KCNQ1) and Kv11.1 (KCNH2) Channel Dysfunction and Long QT Syndrome Phenotypes

The electrocardiogram (ECG) empowered clinician scientists to measure the electrical activity of the heart noninvasively to identify arrhythmias and heart disease. Shortly after the standardization of the 12-lead ECG for the diagnosis of heart disease, several families with autosomal recessive (Jervell and Lange-Nielsen Syndrome) and dominant (Romano-Ward Syndrome) forms of long QT syndrome (LQTS) were identified. An abnormally long heart rate-corrected QT-interval was established as a biomarker for the risk of sudden cardiac death. Since then, the International LQTS Registry was established; a phenotypic scoring system to identify LQTS patients was developed; the major genes that associate with typical forms of LQTS were identified; and guidelines for the successful management of patients advanced. In this review, we discuss the molecular and cellular mechanisms for LQTS associated with missense variants in KCNQ1 (LQT1) and KCNH2 (LQT2). We move beyond the "benign" to a "pathogenic" binary classification scheme for different KCNQ1 and KCNH2 missense variants and discuss gene- and mutation-specific differences in K+ channel dysfunction, which can predispose people to distinct clinical phenotypes (e.g., concealed, pleiotropic, severe, etc.). We conclude by discussing the emerging computational structural modeling strategies that will distinguish between dysfunctional subtypes of KCNQ1 and KCNH2 variants, with the goal of realizing a layered precision medicine approach focused on individuals.

XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein-Ligand Scoring and Ranking

Prediction of protein-ligand binding affinities is a central issue in structure-based computer-aided drug design. In recent years, much effort has been devoted to the prediction of the binding affinity in protein-ligand complexes using machine learning (ML). Due to the remarkable ability of ML methods in nonlinear fitting, ML-based scoring functions (SFs) can deliver much improved performance on a selected test set, such as the comparative assessment of scoring functions (CASF), when compared to the classical SFs. However, the performance of ML-based SFs heavily relies on the overall similarity of the training set and the test set. To improve the performance and transferability of an SF, we have tried to combine various features including energy terms from X-score and AutoDock Vina, the properties of ligands, and the statistical sequence-related information from either the binding site or the full protein. In conjunction with extreme trees (ET), an ML model, we have developed XLPFE, a new SF. Compared with other tested methods such as X-score, AutoDock Vina, ΔvinaXGB, PSH-ML, or CNN-score, XLPFE achieves consistently better scoring and ranking power for various types of protein-ligand complex structures beyond the CASF, suggesting that XLPFE has superior transferability. In particular, XLPFE performs better with metalloenzymes. With its faster speed, improved accuracy, and better transferability, XLPFE could be usefully applied to a diverse range of protein-ligand complexes.

Identifying the Novel Inhibitors Against the Mycolic Acid Biosynthesis Pathway Target "mtFabH" of Mycobacterium tuberculosis

Mycolic acids are the key constituents of mycobacterial cell wall, which protect the bacteria from antibiotic susceptibility, helping to subvert and escape from the host immune system. Thus, the enzymes involved in regulating and biosynthesis of mycolic acids can be explored as potential drug targets to kill Mycobacterium tuberculosis (Mtb). Herein, Kyoto Encyclopedia of Genes and Genomes is used to understand the fatty acid metabolism signaling pathway and integrative computational approach to identify the novel lead molecules against the mtFabH (β-ketoacyl-acyl carrier protein synthase III), the key regulatory enzyme of the mycolic acid pathway. The structure-based virtual screening of antimycobacterial compounds from ChEMBL library against mtFabH results in the selection of 10 lead molecules. Molecular binding and drug-likeness properties of lead molecules compared with mtFabH inhibitor suggest that only two compounds, ChEMBL414848 (C1) and ChEMBL363794 (C2), may be explored as potential lead molecules. However, the spatial stability and binding free energy estimation of thiolactomycin (TLM) and compounds C1 and C2 with mtFabH using molecular dynamics simulation, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) indicate the better activity of C2 (ΔG = -14.18 kcal/mol) as compared with TLM (ΔG = -9.21 kcal/mol) and C1 (ΔG = -13.50 kcal/mol). Thus, compound C1 may be explored as promising drug candidate for the structure-based drug designing of mtFabH inhibitors in the therapy of Mtb.

Application of molecular dynamics simulation in biomedicine

Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein-protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function.

Optimizing peptide inhibitors of SARS-Cov-2 nsp10/nsp16 methyltransferase predicted through molecular simulation and machine learning

Coronaviruses, including the recent pandemic strain SARS-Cov-2, use a multifunctional 2'-O-methyltransferase (2'-O-MTase) to restrict the host defense mechanism and to methylate RNA. The nonstructural protein 16 2'-O-MTase (nsp16) becomes active when nonstructural protein 10 (nsp10) and nsp16 interact. Novel peptide drugs have shown promise in the treatment of numerous diseases and new research has established that nsp10 derived peptides can disrupt viral methyltransferase activity via interaction of nsp16. This study had the goal of optimizing new analogous nsp10 peptides that have the ability to bind nsp16 with equal to or higher affinity than those naturally occurring. The following research demonstrates that in silico molecular simulations can shed light on peptide structures and predict the potential of new peptides to interrupt methyltransferase activity via the nsp10/nsp16 interface. The simulations suggest that misalignments at residues F68, H80, I81, D94, and Y96 or rotation at H80 abrogate MTase function. We develop a new set of peptides based on conserved regions of the nsp10 protein in the Coronaviridae species and test these to known MTase variant values. This results in the prediction that the H80R variant is a solid new candidate for potential new testing. We envision that this new lead is the beginning of a reputable foundation of a new computational method that combats coronaviruses and that is beneficial for new peptide drug development.

A systematic review on active sites and functions of PIM-1 protein

The Proviral Integration of Molony murine leukemia virus (PIM)-1 protein contributes to the solid cancers and hematologic malignancies, cell growth, proliferation, differentiation, migration, and other life activities. Many studies have related these functions to its molecular structure, subcellular localization and expression level. However, recognition of specific active sites and their effects on the activity of this constitutively active kinase is still a challenge. Based on the close relationship between its molecular structure and functional activity, this review covers the specific residues involved in the binding of ATP and different substrates in its catalytic domain. This review then elaborates on the relevant changes in protein conformation and cell functions after PIM-1 binds to different substrates. Therefore, this intensive study can improve the understanding of PIM-1-regulated signaling pathways by facilitating the discovery of its potential phosphorylation substrates.

Machine learning-based prediction of drug and ligand binding in BCL-2 variants through molecular dynamics

Venetoclax is a BH3 (BCL-2 Homology 3) mimetic used to treat leukemia and lymphoma by inhibiting the anti-apoptotic BCL-2 protein thereby promoting apoptosis of cancerous cells. Acquired resistance to Venetoclax via specific variants in BCL-2 is a major problem for the successful treatment of cancer patients. Replica exchange molecular dynamics (REMD) simulations combined with machine learning were used to define the average structure of variants in aqueous solution to predict changes in drug and ligand binding in BCL-2 variants. The variant structures all show shifts in residue positions that occlude the binding groove, and these are the primary contributors to drug resistance. Correspondingly, we established a method that can predict the severity of a variant as measured by the inhibitory constant (Ki) of Venetoclax by measuring the structure deviations to the binding cleft. In addition, we also applied machine learning to the phi and psi angles of the amino acid backbone to the ensemble of conformations that demonstrated a generalizable method for drug resistant predictions of BCL-2 proteins that elucidates changes where detailed understanding of the structure-function relationship is less clear.

PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions

Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning.

Sun et al. present PremPLI, a machine learning approach and web tool to predict how missense mutations in a drug’s target protein will affect the drug’s binding affinity. PremPLI can be applied to identify drug resistance mechanisms in cancer cells and microorganisms and develop drugs to combat drug resistance.

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase

Kinases play crucial roles in cellular signalling and biological processes with their dysregulation associated with diseases, including cancers. Kinase inhibitors, most notably those targeting ABeLson 1 (ABL1) kinase in chronic myeloid leukemia, have had a significant impact on cancer survival, yet emergence of resistance mutations can reduce their effectiveness, leading to therapeutic failure. Limited effort, however, has been devoted to developing tools to accurately identify ABL1 resistance mutations, as well as providing insights into their molecular mechanisms. Here we investigated the structural basis of ABL1 mutations modulating binding affinity of eight FDA-approved drugs. We found mutations impair affinity of type I and type II inhibitors differently and used this insight to developed a novel web-based diagnostic tool, SUSPECT-ABL, to pre-emptively predict resistance profiles and binding free-energy changes (ΔΔG) of all possible ABL1 mutations against inhibitors with different binding modes. Resistance mutations in ABL1 were successfully identified, achieving a Matthew's Correlation Coefficient of up to 0.73 and the resulting change in ligand binding affinity with a Pearson's correlation of up to 0.77, with performances consistent across non-redundant blind tests. Through an in silico saturation mutagenesis, our tool has identified possibly emerging resistance mutations, which offers opportunities for in vivo experimental validation. We believe SUSPECT-ABL will be an important tool not just for improving precision medicine efforts, but for facilitating the development of next-generation inhibitors that are less prone to resistance. We have made our tool freely available at http://biosig.unimelb.edu.au/suspect_abl/.

Visualization of Protein-Drug Interactions for the Analysis of Drug Resistance in Lung Cancer

Non-small cell lung cancer (NSCLC) caused by mutation of the epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) of EGFR have been developed and show promising results at the initial stage of therapy. However, in most cases, their efficacy becomes limited due to the emergence of secondary mutations causing drug resistance after about a year. In this work, we investigated the mechanism of drug resistance due to these mutations. We performed molecular dynamics (MD) simulations of EGFR-drug interactions to obtain Euclidean distance and binding free energy values to analyse drug resistance and visualize drug-protein interactions. A PCA-based method is proposed to find normal, rigid, flexible, and critical residues. We have established a systematic method for the visualization of protein-drug interactions, which provides an effective framework for the analysis of drug resistance in lung cancer at the atomic level.

Biophysical analysis of SARS-CoV-2 transmission and theranostic development via N protein computational characterization

Recently, SARS-CoV-2 has been identified as the causative factor of viral infection called COVID-19 that belongs to the zoonotic beta coronavirus family known to cause respiratory disorders or viral pneumonia, followed by an extensive attack on organs that express angiotensin-converting enzyme II (ACE2). Human transmission of this virus occurs via respiratory droplets from symptomatic and asymptomatic patients, which are released into the environment after sneezing or coughing. These droplets are capable of staying in the air as aerosols or surfaces and can be transmitted to persons through inhalation or contact with contaminated surfaces. Thus, there is an urgent need for advanced theranostic solutions to control the spread of COVID-19 infection. The development of such fit-for-purpose technologies hinges on a proper understanding of the transmission, incubation, and structural characteristics of the virus in the external environment and within the host. Hence, this article describes the development of an intrinsic model to describe the incubation characteristics of the virus under varying environmental factors. It also discusses on the evaluation of SARS-CoV-2 structural nucleocapsid protein properties via computational approaches to generate high-affinity binding probes for effective diagnosis and targeted treatment applications by specific targeting of viruses. In addition, this article provides useful insights on the transmission behavior of the virus and creates new opportunities for theranostics development.

Discovering Potential RNA Dependent RNA Polymerase Inhibitors as Prospective Drugs Against COVID-19: An in silico Approach

COVID-19, caused by Severe Acute Respiratory Syndrome Corona Virus 2, is declared a Global Pandemic by WHO in early 2020. In the present situation, though more than 180 vaccine candidates with some already approved for emergency use, are currently in development against SARS-CoV-2, their safety and efficacy data is still in a very preliminary stage to recognize them as a new treatment, which demands an utmost emergency for the development of an alternative anti-COVID-19 drug sine qua non for a COVID-19 free world. Since RNA-dependent RNA polymerase (RdRp) is an essential protein involved in replicating the virus, it can be held as a potential drug target. We were keen to explore the plant-based product against RdRp and analyze its inhibitory potential to treat COVID-19. A unique collection of 248 plant compounds were selected based on their antiviral activity published in previous literature and were subjected to molecular docking analysis against the catalytic sub-unit of RdRp. The docking study was followed by a pharmacokinetics analysis and molecular dynamics simulation study of the selected best-docked compounds. Tellimagrandin I, SaikosaponinB2, Hesperidin and (-)-Epigallocatechin Gallate were the most prominent ones that showed strong binding affinity toward RdRp. All the compounds mentioned showed satisfactory pharmacokinetics properties and remained stabilized at their respective binding sites during the Molecular dynamics simulation. Additionally, we calculated the free-binding energy/the binding properties of RdRp-ligand complexes with the connection of MM/GBSA. Interestingly, we observe that SaikosaponinB2 gives the best binding affinity (∆Gbinding = -42.43 kcal/mol) in the MM/GBSA assay. Whereas, least activity is observed for Hesperidin (∆Gbinding = -22.72 kcal/mol). Overall our study unveiled the feasibility of the SaikosaponinB2 to serve as potential molecules for developing an effective therapy against COVID-19 by inhibiting one of its most crucial replication proteins, RdRp.

Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.