Deep-learning-based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integration of expert knowledge on protein dynamics. We introduce AFEXplorer, a generic approach that tailors AlphaFold predictions to user-defined constraints in coarse coordinate spaces by optimizing embedding features. Its effectiveness in generating functional protein conformations in accordance with predefined conditions is demonstrated through comprehensive examples. AFEXplorer serves as a versatile platform for conditioned protein structure prediction, bridging the gap between automated models and domain-specific insights. Published by the American Physical Society 2024

Flavonoids are essential for plant growth and environmental adaptation. Chalcone synthase (CHS) directs metabolic flux into flavonoid biosynthesis, but its catalytic promiscuity limits the biosynthetic efficiency. Although the chalcone isomerase-like protein (CHIL) has been characterized as physically interacting with and regulating CHS, the underlying mechanism remains elusive. Here, we report the crystal structure of the CHS-CHIL complex, revealing that CHIL modulates CHS function by gating the substrate-binding pocket entrance through its β-hairpin region. Molecular dynamics simulations indicate that regulation occurs early in the catalytic cycle, affecting substrate binding or early intermediate formation. CHIL enhances CHS activity and product specificity by promoting CoA release and stabilizing key amino acid residues. His36 within the β-hairpin is functionally critical; its substitution with leucine in Arabidopsis and other plant species markedly improves CHS catalytic efficiency and specificity. This regulatory mechanism is evolutionarily conserved across land plants, from bryophytes to angiosperms. Guided by evolutionary analysis, we engineer a CHIL variant (H36E/F37T) that significantly enhances CHS activity. Collectively, our findings establish a conserved binding-conformational regulation paradigm that governs metabolic flux into the flavonoid biosynthetic pathway and provides practical strategies for enhancing flavonoid production and composition in crops.

Ab initio protein structure prediction is one of the most challenging problems in computational biology. Multistage algorithms are widely used in ab initio protein structure prediction. The different computational costs of a multistage algorithm for different proteins are important to be considered. In this study, a population-based algorithm guided by information entropy (PAIE), which includes exploration and exploitation stages, is proposed for protein structure prediction. In PAIE, an entropy-based stage switch strategy is designed to switch from the exploration stage to the exploitation stage. Torsion angle statistical information is also deduced from the first stage and employed to enhance the exploitation in the second stage. Results indicate that an improvement in the performance of protein structure prediction in a benchmark of 30 proteins and 17 other free modeling targets in CASP.

In order to balance the exploration and exploitation ability of differential evolution (DE), different mutation strategy for different evolutionary stages may be effective. An adaptive differential evolution with information entropy-based mutation strategy (DEIE) is proposed to divide the evolutionary process reasonably. In DEIE, the number of Markov states deduced from the crowding strategy is determined first and then the transition matrix between states is inferred from the historical evolutionary information. Based on the above-mentioned knowledge, the Markov state model is constructed. The evolutionary process is divided into exploration and exploitation stages dynamically using the information entropy derived from the Markov state model. Consequently, stage-specific mutation operation is employed adaptively. Experiments are conducted on CEC 2013, 2014, and 2017 benchmark sets and classical benchmark functions to assess the performance of DEIE. Moreover, the proposed approach is also used to solve the protein structure prediction problem efficiently.

Deep learning based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integration of expert knowledge on protein dynamics. We introduce AFEXplorer, a generic approach that tailors AlphaFold predictions to user-defined constraints in coarse coordinate spaces by optimizing embedding features. Its effectiveness in generating functional protein conformations in accordance with predefined conditions were demonstrated through comprehensive examples. AFEXplorer serves as a versatile platform for conditioned protein structure prediction, bridging the gap between automated models and domain-specific insights.

SLC26A11, also known as the kidney and brain anion transporter (KBAT), mediates the transport of multiple anionic substrates across the plasma membrane. It is highly enriched in cerebellar Purkinje cells, where loss of KBAT results in hyperpolarized membrane potentials and increased intrinsic firing frequency, correlating with locomotor dysfunction. Despite its physiological significance, the molecular basis of substrate recognition and transport by KBAT remains elusive. Here, we present the structure of full-length human KBAT in an inward-facing conformation with chloride ions (Cl⁻) bound. Sequence analysis of the substrate-binding pockets uncovers the important residue E320 in KBAT. Protonation of E320 abolishes its interaction with S363 and correlates with Cl⁻ binding in the canonical substrate pocket, as substantiated by molecular dynamics (MD) simulations. Quantitative MD contact analysis delineates the rearrangements of residue interactions underlying the alternative access transport, consistent with an elevator-like transport mechanism. The cross-arranged transmembrane helices of the Gate and Core domains require minimal conformational rearrangement to achieve alternating access to the membrane. Collectively, our study provides a critical foundation for comprehensively understanding the recognition and transport mechanisms of human KBAT, with potential implications for developing therapies for neuronal swelling.