Prediction of protein folding rates from simplified secondary structure alphabet

Thermal Energy Transport through Nonbonded Native Contacts in Protein

Within the protein interior, where we observe various types of interactions, nonuniform flow of thermal energy occurs along the polypeptide chain and through nonbonded native contacts, leading to inhomogeneous transport efficiencies from one site to another. The folded native protein serves not merely as thermal transfer medium but, more importantly, as sophisticated molecular nanomachines in cells. Therefore, we are particularly interested in what sort of "communication" is mediated through native contacts in the folded proteins and how such features are quantitatively depicted in terms of local transport coefficients of heat currents. To address the issue, we introduced a concept of inter-residue thermal conductivity and characterized the nonuniform thermal transport properties of a small globular protein, HP36, using equilibrium molecular dynamics simulation and the Green-Kubo formula. We observed that the thermal transport of the protein was dominated by that along the polypeptide chain, while the local thermal conductivity of nonbonded native contacts decreased in the order of H-bonding > π-stacking > electrostatic > hydrophobic contacts. Furthermore, we applied machine learning techniques to analyze the molecular mechanism of protein thermal transport. As a result, the contact distance, variance in contact distance, and H-bonding occurrence probability during MD simulations are found to be the top three important determinants for predicting local thermal transport coefficients.

Designing a structure-function alphabet of helix based on reduced amino acid clusters

Several simple secondary structures could form complex and diverse functional proteins, meaning that secondary structures may contain a lot of hidden information and are arranged according to certain principles, to carry enough information of functional specificity and diversity. However, these inner information and principles have not been understood systematically. In our study, we designed a structure-function alphabet of helix based on reduced amino acid clusters to describe the typical features of helices and delve into the information. Firstly, we selected 480 typical helices from membrane proteins, zymoproteins, transcription factors, and other proteins to define and calculate the interval range, and the helices are classified in terms of hydrophilicity, charge and length: (1) hydrophobic helix (≤43%), amphiphilic helix (43%∼71%), and hydrophilic helix (≥71%). (2) positive helix, negative helix, electrically neutral helix and uncharged helix. (3) short helix (≤8 aa), medium-length helix (9-28 aa), and long helix (≥29 aa). Then, we designed an alphabet containing 36 triplet codes according to the above classification, so that the main features of each helix can be represented by only three letters. This alphabet not only preliminarily defined the helix characteristics, but also greatly reduced the informational dimension of protein structure. Finally, we present an application example to demonstrate the value of the structure-function alphabet in protein functional determination and differentiation.

PS4: a next-generation dataset for protein single-sequence secondary structure prediction

Protein secondary structure prediction is a subproblem of protein folding. A light-weight algorithm capable of accurately predicting secondary structure from only the protein residue sequence could provide useful input for tertiary structure prediction, alleviating the reliance on multiple sequence alignments typically seen in today's best-performing models. Unfortunately, existing datasets for secondary structure prediction are small, creating a bottleneck. We present PS4, a dataset of 18,731 nonredundant protein chains and their respective secondary structure labels. Each chain is identified, and the dataset is nonredundant against other secondary structure datasets commonly seen in the literature. We perform ablation studies by training secondary structure prediction algorithms on the PS4 training set and obtains state-of-the-art accuracy on the CB513 test set in zero shots.

Key role of Fe(VI)-activated Bi2WO6 in the photocatalytic oxidation of sulfonamides: Mediated electron transfer mechanism

The widespread use of sulfonamides (SAs) in animals and human infections has raised significant concerns regarding their presence in ambient waterways and potential for inducing antimicrobial resistance. Herein, we report on the capacity of ferrate (VI) (Fe^VIO₄^2-, Fe(VI)) to facilitate the photocatalytic degradation of sulfamethazine (SMT) via bismuth tungstate (Bi₂WO₆, BWO) under blue LED light (Vis/BWO/Fe(VI)) exposure, at rates that were 45-fold faster than BWO photocatalysis. Both the stepwise and time-series addition of Fe(VI) contributed to the degradation. Multiple lines of evidence confirmed that the common reactive species (RSs) in BWO-based photocatalytic systems and Fe(VI)-involved systems (e.g., •OH/h⁺, O₂^•-, ¹O₂ and Fe(V)/Fe(IV)) played subtle roles in our study system. Herein, for the first time, it was discovered that the precursor complex (BWO-Fe(V)/Fe(IV)* )) was the main contributor to induce electron transfer of SAs through the "conductive bridge" effect of BWO. The studied system was able to effectively degrade SMT in synthetic hydrolyzed urine (SHU) with low interference from background substances in water. This work not only offers a novel facilitation strategy for BWO, but also holds a great application prospect for contamination remediation in urine.

Evolution as a Guide to Designing xeno Amino Acid Alphabets

Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life’s standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, “Protein Structure Analysis and Prediction with Statistical Scoring Functions”, emerges from the ways in which current bioinformatics currently lacks empirical science when it comes to xenoproteins composed largely or entirely of amino acids from beyond the standard genetic code. Our intent is to present new perspectives on existing data from two different frontiers in order to suggest fresh ways in which their findings complement one another. These frontiers are origins/astrobiology research into the emergence of the standard amino acid alphabet, and empirical xenoprotein synthesis.

Solution of Levinthal's Paradox and a Physical Theory of Protein Folding Times

“How do proteins fold?” Researchers have been studying different aspects of this question for more than 50 years. The most conceptual aspect of the problem is how protein can find the global free energy minimum in a biologically reasonable time, without exhaustive enumeration of all possible conformations, the so-called “Levinthal’s paradox.” Less conceptual but still critical are aspects about factors defining folding times of particular proteins and about perspectives of machine learning for their prediction. We will discuss in this review the key ideas and discoveries leading to the current understanding of folding kinetics, including the solution of Levinthal’s paradox, as well as the current state of the art in the prediction of protein folding times.

Network measures for protein folding state discrimination

Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis.