Advancing Protein Structure and Ligand Binding Studies with Quantum Crystallography

Proteins are the building blocks of all cells in both human and all our living creatures of the world. Most of the work in the living organism is performed by Proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by protein’s tertiary structure. If two protein structures are alike then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used protein structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we analyze the effectiveness of a group of image based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern histogram, Gabor filtered local binary pattern histogram, separate row multiplication matrix with uniform local binary pattern histogram, neighbour block subtraction matrix with uniform local binary pattern histogram and atom bond. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest Random Forest is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-Ligand binding is accountable for managing the tasks of biological receptors that helps to cure diseases and many more. So, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-Ligand Binding Prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we’ve proposed a supervised learning algorithm for predicting Protein-Ligand Binding which is a Similarity-Based Clustering approach using the same set of features. Our algorithm works better than most popular and widely used machine learning algorithms

Protein-ligand interactions are driven by many factors, including protein conformation and pH of the solution. Electrospray mass spectrometry can reveal the degree of protein folding from the distribution of charges imparted to the protein molecule. Additional information about protein-ligand affinity can be derived by fragmenting the complex using MS-MS. Another analytical tool not often combined with mass spectrometry but which can also illuminate information about protein structure and ligand binding is NMR. In the Special Feature the laboratories of Grandori at the University of Milano-Bicocca and Molinari at the University of Verona use these two analytical tools to study the binding of human liver fatty acid binding protein (hL-FABP) with two fatty acids, oleic acid or palmitic acid. The complementarity of the tools are shown. From ligand titrations ESI-MS shows that a maximum number of fatty acid molecules bind with hL-FABP with a peferential affinity for oleic acid over palmitic acid while titration studies with 13C NMR and 2D NMR reveal additional information suggesting polarity interactions drive the binding as well as solvent accessability to the binding sites within the folded protein.

A brief survey of some recent work in protein chemistry

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

Although the hydrogen bond is known to be an important mediator of intermolecular interactions, there has yet to be an analysis of the role of CH...O hydrogen bonds in protein-ligand complexes. In this work, we present evidence for such nonstandard hydrogen bonds from a survey of aromatic ligands in 184 kinase crystal structures and 358 high-resolution structures from the Protein Data Bank. CH groups adjacent to the positively charged nitrogen of nicotinamide exhibit geometric preferences strongly suggestive of hydrogen bonding interactions, as do heterocyclic CH groups in kinase ligands, while other aromatic CH groups do not exhibit these characteristics. Ab initio calculations reveal a considerable range of CH...O hydrogen bonding potentials among different aromatic ring systems, with nicotinamide and heterocycles preferred in kinase inhibitors showing particularly favorable interactions. These results provide compelling evidence for the existence of CH...O hydrogen bonds in protein-ligand interactions, as well as information on the relative strength of various aromatic CH donors. Such knowledge will be of considerable value in protein modeling, ligand design, and structure-activity analysis.

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

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Synthetic ligands of androgen receptor (AR) are a standard in the treatment of androgen deficiency. One of the effects of androgen deficiency is the disturbance in the homeostasis of lipid metabolism. Till date, there are no effective compounds developed to treat androgen deficiency without having any side effects. Nonsteroidal selective androgen receptor modulators (SARMs) are a promising solution for various clinical indications. In this study, we investigated the effect of ostarine (enobosarm), a nonsteroidal SARM, on the rat adipocyte metabolism using in vitro techniques. Isolated rat adipocytes were incubated in the presence of different concentrations of ostarine. Control incubation with testosterone as the natural ligand for AR was performed. AR inhibitors were used to investigate the genomic activity of ostarine. Changes in the intensity of lipolysis, lipogenesis, and the secretion of leptin and adiponectin were measured. Moreover, the gene expression of leptin and adiponectin was assessed. For the first time, we have shown that ostarine has a significant effect on the intensity of lipid metabolism. Ostarine downregulates the expression of leptin and adiponectin mRNAs, as well as decreases their release from rat adipocytes. According to our results, ostarine acts via AR with a similar effect as testosterone in the regulation of lipid metabolism and endocrine function of mature rat adipocytes in vitro. Our results indicate the need for further studies on the effects of SARM on the whole organism.

Protein Proteins Interactions (PPIs) is a process of interacting protein with proteins in order to produce some organic procedures. To make better understanding of recognizing protein work it is necessary to create high throughput strategies for distinguishing PPI. Protein protein interaction is used for different cells also it defines the 3D structure of proteins. It facilitates different cell capacities resulting from 3D structure of cell that is three dimensional structures of protein protein complexes interactions and it binds affinity information together. The purpose of PPIs is to make interactions between bacterial, viral, and parasitic pathogens of human host's harbors which have great medicinal making potential in order to reduce the causes of dieses that occur due to the PPIs in humans. PPIs are used to discover target specific disease with related interfaces that underlines human interaction network. The structure of ligand binding proteins has to face several challenges including protein sampling of the huge possible orientations for ligand, the protein pocket, sequence space of large data and estimating the binding free energies accurately during the design process. Computational methods are used for successful ligand binding protein design their pros and cons and the potential future directions of the field are discussed. In this paper we have analyzed different methods and techniques of PPIs identification, management, interactions and bindings also to gather different analysis and results based on big databases. In future, we will use Spark's distributed machine Learning library for PPIs prediction, data modeling and machine learning.

Heat capacity effects in protein folding and ligand binding: a re-evaluation of the role of water in biomolecular thermodynamics

The field of molecular dynamics (MD) simulations has become indispensable today to studying the conformational flexibility and dynamics of proteins as well as protein–ligand complexes. The technique helps to replicate real-time biological events like macromolecular dynamics on a computational platform and allows us to understand the fold and conformational changes in the protein–ligand complex. In addition, MD simulations enable us to estimate the thermodynamics and kinetics associated with protein–ligand binding. In this chapter, we introduce the basics of MD simulations and the theoretical aspects of the simulations. Further, we describe the sequential steps in the process of MD simulation and the background information of the steps. The chapter also discusses ligand binding and conformational changes with the help of case studies. Though the field has advanced by leaps and bounds, there is still a necessity for better force fields and methods to accurately predict the free energy of binding. In summary, research focusing on force fields supported by advancements in computational power will help researchers have better insights into protein–ligand interactions and their conformations.

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

In drug discovery, determining the binding affinity and functional effects of small-molecule ligands on proteins is critical. Current computational methods can predict these protein–ligand interaction properties but often lose accuracy without high-resolution protein structures and falter in predicting functional effects. Here we introduce PSICHIC (PhySIcoCHemICal graph neural network), a framework incorporating physicochemical constraints to decode interaction fingerprints directly from sequence data alone. This enables PSICHIC to attain capabilities in decoding mechanisms underlying protein–ligand interactions, achieving state-of-the-art accuracy and interpretability. Trained on identical protein–ligand pairs without structural data, PSICHIC matched and even surpassed leading structure-based methods in binding-affinity prediction. In an experimental library screening for adenosine A1 receptor agonists, PSICHIC discerned functional effects effectively, ranking the sole novel agonist within the top three. PSICHIC’s interpretable fingerprints identified protein residues and ligand atoms involved in interactions, and helped in unveiling selectivity determinants of protein–ligand interaction. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein–ligand interactions. Predicting the binding affinity between small-molecule ligands and proteins is a key task in drug discovery; however, sequence-based methods are often less accurate than structure-based ones. Koh et al. develop a graph neural network using physicochemical constraints that discovers interactions between small molecules and proteins directly from sequence data and that can achieve state-of-the-art performance without the need for costly, experimental 3D structures.

The 115-residue hypothetical protein TM1290 is a member of the COG1433 protein family, and it has sequence similarity to NifX and the C-terminal domain of NifB (Shah et al., 1999). These proteins are involved in the biosynthesis of the iron-molybdenum cofactor (FeMo-co) of dinitrogenase, an enzyme which catalyzes the reduction of nitrogen gas to ammonium in an ATPand reductant-dependent reaction (Rubio et al., 2002). This paper documents the first de novo protein structure determination using the algorithms ATNOS (Herrmann et al., 2002a) for automated NOESY peak picking and NOE identification, and CANDID (Herrmann et al., 2002b) for automated NOE assignment, which enables direct feedback between the 3D protein structure and the raw NMR data during protein structure refinement.

Weakly Polar Interactions In Proteins