Computational approaches for the design of modulators targeting protein-protein interactions.

BACKGROUND: Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery. AREAS COVERED: In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes. EXPERT OPINION: Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach.

Cyclin-dependent kinase inhibitor 2 A (CDKN2A) gene belongs to the cyclin-dependent kinase family that code for two transcripts (p16INK4A and p14ARF), both work as tumor suppressors proteins. The mutation that occurs in the p14ARF protein can lead to different types of cancers. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that can lead to different types of diseases. In this study, we applied the computational strategy on human p14ARF protein to identify the potential deleterious nsSNPs and check their impact on the structure, function, and protein stability. We applied more than ten prediction tools to screen the retrieved 288 nsSNPs, consequently extracting four deleterious nsSNPs i.e., rs139725688 (R10G), rs139725688 (R21W), rs374360796 (F23L) and rs747717236 (L124R). Homology modeling, conservation and conformational analysis of mutant models were performed to examine the divergence of these variants from the native p14ARF structure. All-atom molecular dynamics simulation revealed a significant impact of these mutations on protein stability, compactness, globularity, solvent accessibility and secondary structure elements. Protein-protein interactions indicated that p14ARF operates as a hub linking clusters of different proteins and that changes in p14ARF may result in the disassociation of numerous signal cascades. Our current study is the first survey of computational analysis on p14ARF protein that determines the association of these nsSNPs with the altered function of p14ARF protein and leads to the development of various types of cancers. This research proposes the described functional SNPs as possible targets for proteomic investigations, diagnostic procedures, and treatments.Communicated by Ramaswamy H. Sarma.

Hidden allosteric sites and De-Novo drug design.

INTRODUCTION: Hidden allosteric sites are not visible in apo-crystal structures, but they may be visible in holo-structures when a certain ligand binds and maintains the ligand intended conformation. Several computational and experimental techniques have been used to investigate these hidden sites but identifying them remains a challenge. AREAS COVERED: This review provides a summary of the many theoretical approaches for predicting hidden allosteric sites in disease-related proteins. Furthermore, promising cases have been thoroughly examined to reveal the hidden allosteric site and its modulator. EXPERT OPINION: In the recent past, with the development in scientific techniques and bioinformatics tools, the number of drug targets for complex human diseases has significantly increased but unfortunately most of these targets are undruggable due to several reasons. Alternative strategies such as finding cryptic (hidden) allosteric sites are an attractive approach for exploitation of the discovery of new targets. These hidden sites are difficult to recognize compared to allosteric sites, mainly due to a lack of visibility in the crystal structure. In our opinion, after many years of development, MD simulations are finally becoming successful for obtaining a detailed molecular description of drug-target interaction.

Molecular signatures for CCN1, p21 and p27 in progressive mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a comparatively rare non-Hodgkin's lymphoma characterised by overexpression of cyclin D1. Many patients present with or progress to advanced stage disease within 3 years. MCL is considered an incurable disease with median survival between 3 and 4 years. We have investigated the role(s) of CCN1 (CYR61) and cell cycle regulators in progressive MCL. We have used the human MCL cell lines REC1 < G519 < JVM2 as a model for disease aggression. The magnitude of CCN1 expression in human MCL cells is REC1 > G519 > JVM2 cells by RQ-PCR, depicting a decrease in CCN1 expression with disease progression. Investigation of CCN1 isoform expression by western blotting showed that whilst expression of full-length CCN1 was barely altered in the cell lines, expression of truncated forms (18-20 and 28-30 kDa) decreased with disease progression. We have then demonstrated that cyclin D1 and cyclin dependent kinase inhibitors (p21CIP1and p27KIP1) are also involved in disease progression. Cyclin D1 was highly expressed in REC1 cells (OD: 1.0), reduced to one fifth in G519 cells (OD: 0.2) and not detected by western blotting in JVM2 cells. p27KIP1 followed a similar profile of expression as cyclin D1. Conversely, p21CIP1 was absent in the REC1 cells and showed increasing expression in G519 and JVM2 cells. Subcellular localization detected p21CIP1/ p27KIP1 primarily within the cytoplasm and absent from the nucleus, consistent with altered roles in treatment resistance. Dysregulation of the CCN1 truncated forms are associated with MCL progression. In conjunction with reduced expression of cyclin D1 and increased expression of p21, this molecular signature may depict aggressive disease and treatment resistance.