In silico prediction of novel residues involved in amyloid primary nucleation of human I56T and D67H lysozyme

Inhibition of primary and secondary nucleation alongwith disruption of amyloid fibrils and alleviation of associated cytotoxicity: A biophysical insight of a novel property of Chlorpropamide (an anti-diabetic drug)

Aggregation of physiologically synthesized soluble proteins to insoluble, cytotoxic fibrils is a pre-requisite for pathogenesis of amyloid associated disorders including Alzheimer's disease, non-systemic amyloidosis, Parkinson's disease, etc. Considerable advancement has been made to understand the mechanism behind aggregation process but till date we have no efficient cure and preventive therapy for associated diseases. Strategies to prevent protein aggregation are nevertheless many which have been proved promisingly successful in vitro. One of those is repurposing already approved drugs that saves time and money too and has been employed in this study. Here, for the first time we are reporting the effectiveness of an anti-diabetic drug chlorpropamide (CHL) under dosage conditions, a novel property to inhibit aggregation in human lysozyme (HL) in vitro. Spectroscopic (Turbidity, RLS, ThT, DLS, ANS) and microscopic (CLSM) results demonstrates that CHL has the potency to suppress aggregation in HL up to 70 %. CHL is shown to affect the elongation of fibrils with IC₅₀ value of 88.5 μM as clear from the kinetics results, may be by interacting near/with aggregation prone regions of HL. Hemolytic assay also revealed the reduced cytotoxicity in the presence of CHL. Disruption of amyloid fibrils and inhibition of secondary nucleation in the presence of CHL was also evidenced by ThT, CD and CLSM results with reduced cytotoxicity as confirmed by hemolytic assay. We also performed preliminary studies on α-synuclein fibrillation inhibition and surprisingly found that CHL is not just inhibiting the fibrillation but also stabilizing the protein in its native state. These findings insinuate that CHL (anti-diabetic) possess multiple roles and can be a promising drug for developing therapeutic against non-systemic amyloidosis, Parkinson's disease and other amyloid associated disorders.

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

Renal amyloidosis: an update on diagnosis and pathogenesis

Amyloidosis is a diverse group of protein conformational disorder which is caused by accumulation and deposition of insoluble protein fibrils in vital tissues or organs, instigating organ dysfunction. Renal amyloidosis is characterized by the acellular Congo red-positive pathologic deposition of amyloid fibrils within glomeruli and/or the interstitium. It is generally composed of serum amyloid A-related protein or an immunoglobulin light chain; other rare forms lysozyme, gelsolin, fibrinogen alpha chain, transthyretin, apolipoproteins AI/AII/AIV/CII/CIII; and the recently identified form ALECT2. This disease typically manifests with heavy proteinuria, nephrotic syndrome, and finally progression to end-stage renal failure. Early diagnosis of renal amyloidosis is arduous as its symptoms appear in later stages with prominent amyloid deposition. The identification of the correct type of amyloidosis is quite troublesome as it can be confused with another related form. Therefore, the exact typing of amyloid is essential for prognosis, treatment, and correct management of renal amyloidosis. The emanation of new techniques of proteomic analysis, for instance, mass spectroscopy/laser microdissection, has provided greater accuracy in amyloid typing. This in-depth review emphasizes on the clinical features, renal pathological findings, and diagnosis of the AL and non-AL forms of renal amyloidosis.

In silico assessment of human Calprotectin subunits (S100A8/A9) in presence of sodium and calcium ions using Molecular Dynamics simulation approach

Calprotectin is a heterodimeric protein complex which consists of two subunits including S100A8 and S100A9. This protein has a major role in different inflammatory disease and various types of cancers. In current study we aimed to evaluate the structural and thermodynamic changes of the subunits and the complex in presence of sodium and calcium ions using molecular dynamics (MD) simulation. Therefore, the residue interaction network (RIN) was visualized in Cytoscape program. In next step, to measure the binding free energy, the potential of mean force (PMF) method was performed. Finally, the molecular mechanics Poisson-Boltzmann surface area (MMPBSA) method was applied as an effective tool to calculate the molecular model affinities. The MD simulation results of the subunits represented their structural changes in presence of Ca2+. Moreover, the RIN and Hydrogen bond analysis demonstrated that cluster interactions between Calprotectin subunits in presence of Ca2+ were greater in comparison with Na+. Our findings indicated that the binding free energy of the subunits in presence of Ca2+ was significantly greater than Na+. The results revealed that Ca2+ has the ability to induce structural changes in subunits in comparison with Na+ which lead to create stronger interactions between. Hence, studying the physical characteristics of the human proteins could be considered as a powerful tool in theranostics and drug design purposes.

Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs

The conformational dynamics of proteins is rarely used in methodologies used to predict the impact of genetic mutations due to the paucity of three-dimensional protein structures as compared to the vast number of available sequences. Until now a three-dimensional (3D) structure has been required to predict the conformational dynamics of a protein. We introduce an approach that estimates the conformational dynamics of a protein, without relying on structural information. This de novo approach utilizes coevolving residues identified from a multiple sequence alignment (MSA) using Potts models. These coevolving residues are used as contacts in a Gaussian network model (GNM) to obtain protein dynamics. B-factors calculated using sequence-based GNM (Seq-GNM) are in agreement with crystallographic B-factors as well as theoretical B-factors from the original GNM that utilizes the 3D structure. Moreover, we demonstrate the ability of the calculated B-factors from the Seq-GNM approach to discriminate genomic variants according to their phenotypes for a wide range of proteins. These results suggest that protein dynamics can be approximated based on sequence information alone, making it possible to assess the phenotypes of nSNVs in cases where a 3D structure is unknown. We hope this work will promote the use of dynamics information in genetic disease prediction at scale by circumventing the need for 3D structures.

Proteins are dynamic machines that undergo atomic fluctuations, side chain rotations, and collective domain movements that are required for biological function. There is, therefore, a need for quantitative metrics that capture the dynamic fluctuations per position to understand the critical role of protein dynamics in shaping biological functions. A limiting factor in incorporating structural dynamics information in the classification of non-synonymous single nucleotide variants (nSNVs) is the limited number of known 3D structures compared to the vast number of available sequences. We have developed a new sequence-based GNM method, termed Seq-GNM, which uses co-evolving amino acid positions based on the multiple sequence alignment of a given query sequence to estimate the thermal motions of C-alpha atoms. In this paper, we have demonstrated that the predicted thermal motions using Seq-GNM are in reasonable agreement with experimental B-factors as well as B-factors computed using 3D crystal structures. We also provide evidence that B-factors predicted by Seq-GNM are capable of distinguishing between disease-associated and neutral nSNVs.