Investigating Local Sequence-Structural Attributes of Amyloidogenic Light Chain Variable Domains.

Light chain amyloidosis is a medical condition characterized by the aggregation of misfolded antibody light chains into insoluble amyloid fibrils in the target organs, causing organ dysfunction, organ failure, and death. Despite extensive research to understand the factors contributing to amyloidogenesis, accurately predicting whether a given protein will form amyloids under specific conditions remains a formidable challenge. In this study, we have conducted a comprehensive analysis to understand the amyloidogenic tendencies within a dataset containing 1828 (348 amyloidogenic and 1480 non-amyloidogenic) antibody light chain variable region (V) sequences obtained from the AL-Base database.

Structural Implications of HIV-1 Protease Subtype C Bound to Darunavir: A Molecular Dynamics Study.

In recent years, Human Immunodeficiency Virus (HIV) remains a significant global health challenge, with millions affected worldwide, particularly in Africa and sub-Saharan regions. Despite advances in antiretroviral therapies, the genetic variability of HIV, including different subtypes and drug-resistant strains, poses persistent obstacles in the development of universally effective treatments. This study focuses on the dynamics of HIV protease, a key enzyme in viral replication and maturation, particularly targeting subtype C and its double insertion (HL) variant L38HL, in the context of interaction with Darunavir (DRV), a second-generation nonpeptidic protease inhibitor approved by the FDA in 2006.

ZFP-CanPred: Predicting the effect of mutations in zinc-finger proteins in cancers using protein language models.

Zinc-finger proteins (ZNFs) constitute the largest family of transcription factors and play crucial roles in various cellular processes. Missense mutations in ZNFs significantly alter protein-DNA interactions, potentially leading to the development of various types of cancers. This study presents ZFP-CanPred, a novel deep learning-based model for predicting cancer-associated driver mutations in ZNFs.

PRA-MutPred: Predicting the Effect of Point Mutations in Protein-RNA Complexes Using Structural Features.

Interactions between proteins and RNAs are essential for the proper functioning of cells, and mutations in these molecules may lead to diseases. These protein mutations alter the strength of interactions between the protein and RNA, generally described as binding affinity (Δ). Hence, the affinity change upon mutation (ΔΔ) is an important parameter for understanding the effect of mutations in protein-RNA complexes.

Progress on the development of prediction tools for detecting disease causing mutations in proteins.

Proteins are involved in a variety of functions in living organisms. The mutation of amino acid residues in a protein alters its structure, stability, binding, and function, with some mutations leading to diseases. Understanding the influence of mutations on protein structure and function help to gain deep insights on the molecular mechanism of diseases and devising therapeutic strategies.

Protein-nucleic acid complexes: Docking and binding affinity.

Protein-nucleic interactions play essential roles in several biological processes, such as gene regulation, replication, transcription, repair and packaging. The knowledge of three-dimensional structures of protein-nucleic acid complexes and their binding affinities helps to understand these functions. In this review, we focus on two major aspects namely, (i) deciphering the three-dimensional structures of protein-nucleic acid complexes and (ii) predicting their binding affinities.

Tracing ALS Degeneration: Insights from Spinal Cord and Cortex Transcriptomes.

Background/objectives: Amyotrophic Lateral Sclerosis is a progressive neurodegenerative disorder characterized by the loss of upper and lower motor neurons. Key factors contributing to neuronal death include mitochondrial energy damage, oxidative stress, and excitotoxicity. The frontal cortex is crucial for action initiation, planning, and voluntary movements whereas the spinal cord facilitates communication with the brain, walking, and reflexes.

Bioinformatics Approaches for Understanding the Binding Affinity of Protein-Nucleic Acid Complexes.

Protein-nucleic acid interactions are involved in various biological processes such as gene expression, replication, transcription, translation, and packaging. Understanding the recognition mechanism of the protein-nucleic acid complexes has been investigated from different perspectives, including the binding affinities of protein-DNA and protein-RNA complexes. Experimentally, protein-nucleic acid interactions are analyzed using X-ray crystallography, Isothermal Titration Calorimetry (ITC), DNA/RNA pull-down assays, DNA/RNA footprinting, and systematic evolution of ligands by exponential enrichment (SELEX).

Structural and Functional Studies on HIV Protease: Mechanism of Action, Subtypes, Inhibitors, and Drug Resistance.

Human immunodeficiency virus (HIV) targets the host immune system causing acquired immunodeficiency syndrome (AIDS). Although significant advancements have been made on investigating HIV and related infections, eradicating the virus from the host immune system is still challenging. Nevertheless, the combination therapies using drugs targeting different stages in the viral life cycle are used for treatment in which HIV protease plays a vital role.

MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes.

Mutations in the interface of membrane protein (MP) complexes are key contributors to a broad spectrum of human diseases, primarily due to changes in their binding affinities. While various methods exist for predicting the mutation-induced changes in binding affinity (ΔΔG) in protein-protein complexes, none are specific to MP complexes. This study proposes a novel strategy for ΔΔG prediction in MP complexes, which combines linear and nonlinear models, to obtain a more robust model with improved prediction accuracy.

Combinatorial Effects of 5-Fluorouracil and Menadione on Wnt/β-Catenin Pathway in Human Colorectal Cancer Cells.

The incidence and mortality rates of colorectal cancer (CRC) are alarmingly high, and the scientific community is consistently engaged in developing newer therapeutic options for cancer cure or prevention. The fluoropyrimidine drug, 5-fluorouracil (5FU), remains the first line of treatment against CRC; nevertheless, relapses frequently occur since the cells gain resistance over time through various mechanisms. Studies have highlighted the significance of combinatorial treatment of a Wnt signaling inhibitor and 5FU as a better treatment strategy to overcome 5FU resistance.

rAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering.

Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis.

Role of HSP90 in Type 2 Diabetes Mellitus and Its Association with Liver Diseases.

Non-alcoholic fatty acid liver disease (NAFLD), non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC) are the fatal liver diseases which encompass a spectrum of disease severity associated with increased risk of type 2 diabetes mellitus (T2DM), a metabolic disorder. Heat shock proteins serve as markers in early prognosis and diagnosis of early stages of liver diseases associated with metabolic disorder. This review aims to comprehensively investigate the significance of HSP90 isoforms in T2DM and liver diseases.

DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation.

Motivation: Protein-protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein-protein complexes.

Results: We developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently.

Computational approaches for identifying disease-causing mutations in proteins.

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies.

Mechanism of drug resistance in HIV-1 protease subtype C in the presence of Atazanavir.

AIDS is one of the deadliest diseases in the history of humankind caused by HIV. Despite the technological development, curtailing the viral infection inside human host still remains a challenge. Therapies such as HAART uses a combination of drugs to inhibit the viral activity.

CarbDisMut: database on neutral and disease-causing mutations in human carbohydrate-binding proteins.

Protein-carbohydrate interactions are involved in several cellular and biological functions. Integrating structure and function of carbohydrate-binding proteins with disease-causing mutations help to understand the molecular basis of diseases. Although databases are available for protein-carbohydrate complexes based on structure, binding affinity and function, no specific database for mutations in human carbohydrate-binding proteins is reported in the literature.

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions.

PRA-Pred: Structure-based prediction of protein-RNA binding affinity.

Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity.

Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation.

COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus.

MPA-Pred: A machine learning approach for predicting the binding affinity of membrane protein-protein complexes.

Membrane protein-protein interactions are essential for several functions including cell signaling, ion transport, and enzymatic activity. These interactions are mainly dictated by their binding affinities. Although several methods are available for predicting the binding affinity of protein-protein complexes, there exists no specific method for membrane protein-protein complexes.

Investigating Neuron Degeneration in Huntington's Disease Using RNA-Seq Based Transcriptome Study.

Huntington's disease (HD) is a progressive neurodegenerative disorder caused due to a CAG repeat expansion in the huntingtin () gene. The primary symptoms of HD include motor dysfunction such as chorea, dystonia, and involuntary movements. The primary motor cortex (BA4) is the key brain region responsible for executing motor/movement activities.

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data.