Computational Biology and Chemistry

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space...

The methylation and demethylation of lysine and arginine side chains are fundamental processes in gene regulation and disease development. Histone lysine methylation, controlled by histone lysine methyltransferases (KMTs) and histone lysine demethylases (KDMs), plays a vital role in maintaining cellular homeostasis and has been implicated in diseases such as cancer and aging. This study focuses on two members of the lysine demethylase (KDM) family, KDM4E and KDM6B, which are significant in gene regulation and disease pathogenesis...

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task...

Artificial intelligence (AI) has revolutionized structural biology by predicting protein 3D structures with near-experimental accuracy. Here, short backbone N-O distances in high-resolution crystal structures were compared to those in three-dimensional models based on AI AlphaFold/ColabFold, specifically considering their estimated standard errors. Experimental and computationally modeled distances very often differ significantly, showing that these models' precision is inadequate to reproduce experimental results at high resolution...

Due to its emerging resistance to first-line anti-TB medications, tuberculosis (TB) is one of the most contagious illness in the world. According to reports, the effectiveness of treating TB is severely impacted by drug resistance, notably resistance caused by mutations in the pncA gene-encoded pyrazinamidase (PZase) to the front-line drug pyrazinamide (PZA). The present study focused on investigating the resistance mechanism caused by the mutations D12N, T47A, and H137R to better understand the structural and molecular events responsible for the resistance acquired by the pncA gene of Mycobacterium tuberculosis (MTB) at the structural level...

Chemical-Disease relation (CDR) extraction aims to identify the semantic relations between chemical and disease entities in the unstructured biomedical document, which provides a basis for downstream tasks such as clinical medical diagnosis and drug discovery. Compared with general domain relation extraction, it needs a more effective representation of the whole document due to the specialized nature of texts in the biomedical domain, including the biomedical entity and entity-pair representation. In this paper, we propose a novel Multi-view Merge Representation (MMR) model to thoroughly capture entity and entity-pair representation of the document...

Developing new drugs is an expensive, time-consuming process that frequently involves safety concerns. By discovering novel uses for previously verified drugs, drug repurposing helps to bypass the time-consuming and costly process of drug development. As the largest family of proteins targeted by verified drugs, G protein-coupled receptors (GPCR) are vital to efficiently repurpose drugs by inferring their associations with drugs. Drug repurposing may be sped up by computational models that predict the strength of novel drug-GPCR pairs interaction...

COVID-19, a novel ailment, has received comparatively fewer drugs for its treatment. Side Effects (SE) of a COVID-19 drug could cause long-term health issues. Hence, SE prediction is essential in COVID-19 drug development. Efficient models are also needed to predict COVID-19 drug SE since most existing research has proposed many classifiers to predict SE for diseases other than COVID-19. This work proposes a novel classifier based on neighbors named K1 K2 Nearest Neighbors (K1 K2 NN) to predict the SE of the COVID-19 drug from 17 molecules' descriptors and the chemical 1D structure of the drugs...

Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases...

Cancer is one of the wide-ranging diseases which have a high mortality rate impacting globally. This scenario can be switched by early detection and correct precision treatment, a major concern for cancer patients. Clinicians can figure out the best-suited treatments for cancer patients by analyzing the patient's genome, which will treat the patient well and minimize the chances of side effects as well. Therefore, we have developed a fast, robust, and efficient solution as our precision oncology framework based on the whole genome sequencing of the individual's DNA...

Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsin-like proteases and their ligands...

Diabetic mellitus (DM) is a chronic disorder, and type 2 DM (T2DM) is the most prevalent among all categories (nearly 90%) across the globe every year. With the availability of potential drugs, the prevalence rate has remained uncontrollable, while natural resources showed a promising potency, and exploring such potential candidates at the preclinical stage is essential. An extensive literature search selected 89 marine and plant-derived indole derivatives with anti-inflammatory, antioxidant, lipid-lowering, etc...

Accurate classification of membrane proteins like ion channels and transporters is critical for elucidating cellular processes and drug development. We present DeepPLM_mCNN, a novel framework combining Pretrained Language Models (PLMs) and multi-window convolutional neural networks (mCNNs) for effective classification of membrane proteins into ion channels and ion transporters. Our approach extracts informative features from protein sequences by utilizing various PLMs, including TAPE, ProtT5_XL_U50, ESM-1b, ESM-2_480, and ESM-2_1280...

The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN...

Molecular dynamics(MD) simulations are essential for molecular structure optimization, drug-drug interactions, and other fields of drug discovery by simulating the motion of microscopic particles to calculate their macroscopic properties (e.g., energy). The main problems of the existing work are as follows: (1) Failure to fully consider the chemical bonding constraints between atoms, (2) Group equivariance can help achieve robust and accurate predictions of MD under arbitrary reference transformations and should be incorporated into the model design, (3) Tensor information such as relative position, velocity, and torsion angle can be used to enhance the prediction of molecular dynamics...

Alpha-glucosidase (maltase, sucrase, isomaltase and glucoamylase) activities which are involved in carbohydrate metabolism are present in human intestinal maltase-glucoamylase (MGAM) and sucrase-isomaltase (SI). Hence, these proteins are important targets to identify drugs against postprandial hyperglycemia thereby for diabetes. To find natural-based drugs against MGAM and SI, Artocarpus heterophyllus leaf was explored for MGAM and SI inhibition in in vitro and in silico. A. heterophyllus leaf aqueous active fraction (AHL-AAF) was prepared using Soxhlet extraction followed by silica column chromatography...

Amidst the Zn2+ -dependant isoforms of the HDAC family, HDAC6 has emerged as a potential target associated with an array of diseases, especially cancer and neuronal disorders like Rett's Syndrome, Alzheimer's disease, Huntington's disease, etc. Also, despite the availability of a handful of HDAC inhibitors in the market, their non-selective nature has restricted their use in different disease conditions. In this situation, the development of selective and potent HDAC6 inhibitors will provide efficacious therapeutic agents to treat different diseases...

Endocannabinoid system plays a pivotal role in controlling neuroinflammation, and modulating this system may not only aid in managing symptoms of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, Multiple sclerosis, Epilepsy, Central and Peripheral neuropathic pain, but also, have the potential to target these diseases at an early-stage. In the present study, six different pharmacophore hypotheses were generated from Cannabidiol (CBD)-Cannabinoid Receptor subtype-2 (CB2) and then Zinc database was screened for identification of hit molecules...

Read mapping as the foundation of computational biology is a bottleneck task under the pressure of sequencing throughput explodes. In this work, we present an efficient Burrows-Wheeler transform-based aligner for next-generation sequencing (NGS) short read. Firstly, we propose a difference-aware classification strategy to assign specific reads to the computationally more economical search modes, and present some acceleration techniques, such as a seed pruning method based on the property of maximum coverage interval to reduce the redundant locating for candidate regions, redesigning LF calculation to support fast query...

Accumulating clinical studies have consistently demonstrated that the microbes in the human body closely interact with the human host, actively participating in the regulation of drug effectiveness. Identifying the associations between microbes and drugs can facilitate the development of drug discovery, and microbes have become a new target in antimicrobial drug development. However, the discovery of microbe-drug associations relies on clinical or biological experiments, which are not only time-consuming but also financially burdensome...