SARS-CoV-2: Prediction of critical ionic amino acid mutations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that caused coronavirus disease 2019 (COVID-19), has been studied thoroughly, and several variants are revealed across the world with their corresponding mutations. Studies and vaccines development focus on the genetic mutations of the S protein due to its vital role in allowing the virus attach and fuse with the membrane of a host cell. In this perspective, we study the effects of all ionic amino acid mutations of the SARS-CoV-2 viral spike protein S1 when bound to Antibody CC12.1 within the SARS-CoV-2:CC12.1 complex model. Binding free energy calculations between SARS-CoV-2 and antibody CC12.1 are based on the Analysis of Electrostatic Similarities of Proteins (AESOP) framework, where the electrostatic potentials are calculated using Adaptive Poisson-Boltzmann Solver (APBS). The atomic radii and charges that feed into the APBS calculations are calculated using the PDB2PQR software. Our results are the first to propose in silico potential life-threatening mutations of SARS-CoV-2 beyond the present mutations found in the five common variants worldwide. We find each of the following mutations: K378A, R408A, K424A, R454A, R457A, K458A, and K462A, to play significant roles in the binding to Antibody CC12.1, since they are turned into strong inhibitors on both chains of the S1 protein, whereas the mutations D405A, D420A, and D427A, show to play important roles in this binding, as they are turned into mild inhibitors on both chains of the S1 protein.

Mutational signatures in GATA3 transcription factor and its DNA binding domain that stimulate breast cancer and HDR syndrome.

Transcription factors (TFs) play important roles in many biochemical processes. Many human genetic disorders have been associated with mutations in the genes encoding these transcription factors, and so those mutations became targets for medications and drug design. In parallel, since many transcription factors act either as tumor suppressors or oncogenes, their mutations are mostly associated with cancer. In this perspective, we studied the GATA3 transcription factor when bound to DNA in a crystal structure and assessed the effect of different mutations encountered in patients with different diseases and phenotypes. We generated all missense mutants of GATA3 protein and DNA within the adjacent and the opposite GATA3:DNA complex models. We mutated every amino acid and studied the new binding of the complex after each mutation. Similarly, we did for every DNA base. We applied Poisson-Boltzmann electrostatic calculations feeding into free energy calculations. After analyzing our data, we identified amino acids and DNA bases keys for binding. Furthermore, we validated those findings against experimental genetic data. Our results are the first to propose in silico modeling for GATA:DNA bound complexes that could be used to score effects of missense mutations in other classes of transcription factors involved in common and genetic diseases.

Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application.

Degradomics is a novel discipline that involves determination of the proteases/substrate fragmentation profile, called the substrate degradome, and has been recently applied in different disciplines. A major application of degradomics is its utility in the field of biomarkers where the breakdown products (BDPs) of different protease have been investigated. Among the major proteases assessed, calpain and caspase proteases have been associated with the execution phases of the pro-apoptotic and pro-necrotic cell death, generating caspase/calpain-specific cleaved fragments. The distinction between calpain and caspase protein fragments has been applied to distinguish injury mechanisms. Advanced proteomics technology has been used to identify these BDPs experimentally. However, it has been a challenge to identify these BDPs with high precision and efficiency, especially if we are targeting a number of proteins at one time. In this chapter, we present a novel bioinfromatic detection method that identifies BDPs accurately and efficiently with validation against experimental data. This method aims at predicting the consensus sequence occurrences and their variants in a large set of experimentally detected protein sequences based on state-of-the-art sequence matching and alignment algorithms. After detection, the method generates all the potential cleaved fragments by a specific protease. This space and time-efficient algorithm is flexible to handle the different orientations that the consensus sequence and the protein sequence can take before cleaving. It is O(mn) in space complexity and O(Nmn) in time complexity, with N number of protein sequences, m length of the consensus sequence, and n length of each protein sequence. Ultimately, this knowledge will subsequently feed into the development of a novel tool for researchers to detect diverse types of selected BDPs as putative disease markers, contributing to the diagnosis and treatment of related disorders.

Novel Bioinformatics-Based Approach for Proteomic Biomarkers Prediction of Calpain-2 &Caspase-3 Protease Fragmentation: Application to ßII-Spectrin Protein.

The crucial biological role of proteases has been visible with the development of degradomics discipline involved in the determination of the proteases/substrates resulting in breakdown-products (BDPs) that can be utilized as putative biomarkers associated with different biological-clinical significance. In the field of cancer biology, matrix metalloproteinases (MMPs) have shown to result in MMPs-generated protein BDPs that are indicative of malignant growth in cancer, while in the field of neural injury, calpain-2 and caspase-3 proteases generate BDPs fragments that are indicative of different neural cell death mechanisms in different injury scenarios. Advanced proteomic techniques have shown a remarkable progress in identifying these BDPs experimentally. In this work, we present a bioinformatics-based prediction method that identifies protease-associated BDPs with high precision and efficiency. The method utilizes state-of-the-art sequence matching and alignment algorithms. It starts by locating consensus sequence occurrences and their variants in any set of protein substrates, generating all fragments resulting from cleavage. The complexity exists in space O(mn) as well as in O(Nmn) time, where N, m, and n are the number of protein sequences, length of the consensus sequence, and length per protein sequence, respectively. Finally, the proposed methodology is validated against ßII-spectrin protein, a brain injury validated biomarker.

Electrostatic study of Alanine mutational effects on transcription: application to GATA-3:DNA interaction complex.

Protein-DNA interaction is of fundamental importance in molecular biology, playing roles in functions as diverse as DNA transcription, DNA structure formation, and DNA repair. Protein-DNA association is also important in medicine; understanding Protein-DNA binding kinetics can assist in identifying disease root causes which can contribute to drug development. In this perspective, this work focuses on the transcription process by the GATA Transcription Factor (TF). GATA TF binds to DNA promoter region represented by `G,A,T,A' nucleotides sequence, and initiates transcription of target genes. When proper regulation fails due to some mutations on the GATA TF protein sequence or on the DNA promoter sequence (weak promoter), deregulation of the target genes might lead to various disorders. In this study, we aim to understand the electrostatic mechanism behind GATA TF and DNA promoter interactions, in order to predict Protein-DNA binding in the presence of mutations, while elaborating on non-covalent binding kinetics. To generate a family of mutants for the GATA:DNA complex, we replaced every charged amino acid, one at a time, with a neutral amino acid like Alanine (Ala). We then applied Poisson-Boltzmann electrostatic calculations feeding into free energy calculations, for each mutation. These calculations delineate the contribution to binding from each Ala-replaced amino acid in the GATA:DNA interaction. After analyzing the obtained data in view of a two-step model, we are able to identify potential key amino acids in binding. Finally, we applied the model to GATA-3:DNA (crystal structure with PDB-ID: 3DFV) binding complex and validated it against experimental results from the literature.

Bioinformatics approach to understanding interacting pathways in neuropsychiatric disorders.

Bioinformatics-based applications have been incorporated into several medical disciplines, including cancer, neuroscience, and recently psychiatry. Both the increasing interest in the molecular aspect of neuropsychiatry and the availability of high-throughput discovery and analysis tools have encouraged the incorporation of bioinformatics and neurosystems biology techniques into psychiatry and neuroscience research. As applied to neuropsychiatry, systems biology involves the acquisition and processing of high-throughput datasets to infer new information. A major component in bioinformatics output is pathway analysis that provides an insight into and prediction of possible underlying pathogenic processes which may help understand disease pathogenesis. In addition, this analysis serves as a tool to identify potential biomarkers implicated in these disorders. In this chapter, we summarize the different tools and algorithms used in pathway analysis along with their applications to the different layers of molecular investigations, from genomics to proteomics.

Electrostatic exploration of the C3d-FH4 interaction using a computational alanine scan.

The complement system is a component of innate immunity and is activated by a cascade of protein interactions whose function is vital to our ability to fight infection. When proper regulation fails, the complement system is unable to recognize "self" from "nonself" and, therefore, attacks own tissues leading to autoimmune diseases. The central protein of the complement system is C3, which is the convergence point of three independently activated but communicating pathways. Regulation of C3 occurs through modular proteins which consist of many repeats of complement control protein (CCP) modules. CCP modules have diverse sequences, similar structures, and diverse physicochemical compositions, with excess of charge being a predominant characteristic. The goal of our study is to understand the electrostatic mechanism that underlies the interaction between the C3d domain of C3 and the fourth module of the complement regulator Factor H (FH4). We have performed a computational alanine scan in which we have replaced every ionizable amino acid, one at a time, with an alanine to generate a family of mutants for the C3d-FH4 complex. We have used Poisson-Boltzmann electrostatic calculations in combination with clustering of spatial distributions of electrostatic potentials and free energy calculations to delineate the contribution of each replaced amino acid to the C3d-FH4 interaction. We have analyzed our data in view of a two-step model which separates association into long-range recognition and short-range binding and we have identified key amino acids that contribute to association. We discuss the complex role of C3d in binding FH4 and the bacterial proteins Efb/Ehp from Staphylococcus aureus.