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High-Throughput Sequencing on a Next Generation Sequencer to Identify Specific Binders from a Phage Library

Antibody Solution Viscosity and Intermolecular Interactions: Considerations for Development of Highly Concentrated Formulations

Display of Membrane Proteins on a Viral Envelope for Antibody Generation

Sequence and Structural Determinants of Antigen Binding in Antibody CDR Loops

Enhancement of the Stability of Single Chain Fv Molecules with the Amino Acid Substitutions Predicted by High-Performance Computer

Thermal Stability of Camelid Single Domain VHH Antibody

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%–30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present TOPTMH, a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.

The ten years since the first publication of the structure from bovine heart mitochondria in 1997 have significantly broadened our structural knowledge of cytochrome bc₁ and b₆f complexes from various organisms under a variety of conditions providing unprecedented mechanistic insights into the function of these essential enzymes. Still many questions remain. The bifurcated electron transfer at the quinol oxidation (Q_P) site is one of the most difficult and unresolved problems. Intertwined with it, the proton translocation pathway and the quinol oxidation chemistry have remained focuses of intense research. Structural studies of mitochondrial bc₁ complexes have not only provided an atomic view of the bc₁ complex, defining many critical, functionally important features such as the locations of the Q_P and Q_N sites, but have also offered a number of important clues to mechanistic issues leading to the formulation of the "conformational switch of ISP" hypothesis. Intensive biochemical, genetic, and biophysical studies coupled with structural investigations have provided strong support for this hypothesis. The recent structure determination of the bc₁ from the photosynthetic bacterium R. sphaeroides promises further insight.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes COVID-19, a disease currently spreading around the world. Some drugs are underway or being used to combat this disease. Several proteins of the virus can be targeted in therapeutic approaches. Two structural proteins, membrane (M), envelope (E) have critical roles in virus life cycle, such as assembly, budding, envelope formation and pathogenesis. Here, we employed the in silico strategies to identify and evaluate the selected potential compounds against M and E proteins. For this, the structures of proteins were modeled and then several groups of compounds as FDA approved, natural products or under clinical trials were screened from DrugBank and ZINC databases. The selected dockings were analyzed and the ligands with best binding affinity scores were subjected to evaluate drug-likeness and medicinal chemistry friendliness through prediction of ADMET properties. Normal mode analyses were also performed for six selected complexes to explore the collective motions of proteins. Molecular dynamic (MD) simulation was also performed to calculate the stability of two docked protein–ligand complexes. The results revealed that several compounds had high affinity to the proteins along with some acceptable profiles of mobility and deformability, especially, for M protein.

A simple, fast, real-time, and nondestructive analysis of protein expression in biological samples, such as membranes, based on dielectrophoresis is described. On the basis of the distinct differences in the dielectrophoretic properties of individual cell types, the wild-type BabA-positive Helicobacter pylori isolates and its BabA-negative isogenic mutant can be identified and separated. The herein-presented approach of using microelectrodes should be an easy-to-use, cheap, and rapid alternative to separate and distinguish the presence or absence of important outer-membrane proteins.

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker's yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps.