In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.
An Automatable Analytical Algorithm for Structure-Based Protein Functional Annotation via Detection of Specific Ligand 3D Binding Sites: Application to ATP (ser/thr Protein Kinases) and GTP (Small Ras-type G-Proteins) Binding Sites
Prediction of Flavin Mononucleotide (FMN) Binding Sites in Proteins Using the 3D Search Motif Method and Double-Centroid Reduced Representation of Protein 3D Structures
There is great interest to develop artificial intelligence-based protein-ligand affinity models due to their immense applications in drug discovery. In this paper, PointNet and PointTransformer, two pointwise multi-layer perceptrons have been applied for protein-ligand affinity prediction for the first time. Three-dimensional point clouds could be rapidly generated from the data sets in PDBbind-2016, which contain 3 772 and 11 327 individual point clouds derived from the refined or/and general sets, respectively. These point clouds were used to train PointNet or PointTransformer, resulting in …
Two Complementary Methods for Relative Quantification of Ligand Binding Site Burial Depth in Proteins: The "Cutting Plane" and "Tangent Sphere" Methods
The Computation of Cyclic Peptide with Prolin-Prolin Bond as Fusion Inhibitor of DENV Envelope Protein through Molecular Docking and Molecular Dynamics Simulation
Implementation of The Double-Centroid Reduced Representation of Proteins and its Application to the Prediction of Ligand Binding Sites and Protein-Protein Interaction Partners Using FORTRAN 77/90 Language
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by the binding ligand. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a novel method that represents protein behavioral change upon ligand binding with a simple feature that can be used to predict protein-ligand affinity. From unbiased molecular simulation data, an unsupervis…
Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical a…
Persistent local Laplacian prediction of protein-ligand binding affinities
Mathematical Science Research Center, Chongqing University of Technology（重庆理工大学数学科学研究中心）；Department of Mathematics and Statistics, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校数学与统计系）
Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular, the ligand's true binding pose is often scored very unfavorably due to apparent clashes between ligand and protein atoms, which lead to extremely high values of the calculated van der Waals energy term. Traditionally, this problem has been addressed by explicitly searching for receptor conformations to account for the flexibility of the receptor in ligand binding. Here we present a deep learning model trained to ta…
Although algebraic graph theory based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties when compared with other quantitative approaches. There is a need to explore the capability and limitation of algebraic graph theory for molecular and biomolecular modeling, analysis, and prediction. In this work, we propose novel algebraic graph learning (AGL) models that encode high-dimensional physical and biological information into intrinsically low-dimensional representations. The propose…
Implementation of the Tangent Sphere and Cutting Plane Methods in the Quantitative Determination of Ligand Binding Site Burial Depths in Proteins Using FORTRAN 77/90 Language
配体埋藏深度是蛋白质柔性的指标，因为结合配体所需的受体构象变化的程度通常直接随其埋藏深度而变化。在一篇配套论文中（Reyes，V.M. 2015年a），我们报告了切线球（TS）和切割平面（CP）方法-定量的互补方法，独立于蛋白质大小，蛋白质受体中配体埋葬的程度。在这份报告中，我们提出的结果，证明了一组FORTRAN 77和90源代码的有效性，用于实现两个相关的程序，以及精确的实施程序。特别是，我们在这里表明，TS和CP方法的理论模型蛋白质的球形网格点的形式的应用程序准确地描绘了TS和CP指数的行为，从这两种方法获得的预测参数。我们还表明，TS和CP方法对6种蛋白质受体的实施结果（Laskowski et al. 1996）与他们关于这些蛋白质中空腔大小的发现不一致。我们在这里介绍的六个FORTRAN程序是：find_molec_centr.f、tangent_sphere.f、find_CP_coeffs.f、CPM_Neg_Side.f、CPM_Pos_Side.f和CPM_Zero_Side. f。第一个程序计算蛋白质的分子几何质心（全局质心，GC）（TS的中心）的x、y和z坐标。其半径是GC和局部质心（LC）、结合配体的质心或其结合位点的一部分之间的距离。第二个程序计算TS内部、外部和上的蛋白质原子数。第三步确定CP方程的四个系数A、B、C和D，Ax + By + Cz + …
Objective: Total 186 biologically important phenylpropanoids and polyketides compounds from different Indian medicinal plants and dietary sources were screened to filter potential compounds that bind at the active site of the therapeutic target proteins of SARS-CoV-2. Method: The molecular docking studies were carried out by using the Autodock Vina. The in silico ADMET and drug-likeness properties of the compounds were predicted from SwissADME server. Result: The molecular docking study of the 186 compounds with the therapeutic target proteins (Mpro, PLpro, RdRp, SGp and ACE2) of SARS-CoV-2 re…
Ligand diffusion through proteins is a fundamental process governing biological signaling and enzymatic catalysis. The complex topology of protein tunnels results in difficulties with computing ligand escape pathways by standard molecular dynamics (MD) simulations. Here, two novel methods for searching of ligand exit pathways and cavity exploration are proposed: memory random acceleration MD (mRAMD), and memetic algorithms (MA). In mRAMD, finding exit pathways is based on a non-Markovian biasing that is introduced to optimize the unbinding force. In MA, hybrid learning protocols are exploited …
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Tsinghua University（清华大学）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）；University of Washington（华盛顿大学）；Georgia Institute of Technology（佐治亚理工学院）；Helixon Research（Helixon研究院）
Binding kinetic parameters can be correlated with drug efficacy, which led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms in recent years. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of…
Binding of ligands is often crucial for function yet the effects of ligand binding on the mechanical stability and energy landscape of proteins are incompletely understood. Here we use a combination of single-molecule optical tweezers and MD simulations to investigate the effect of ligand binding on the energy landscape of acyl-coenzyme A (CoA) binding protein (ACBP). ACBP is a topologically simple and highly conserved four-alpha-helix bundle protein that acts as an intracellular transporter and buffer for fatty-acyl CoA and is active in membrane assembly. We have previously described the beha…
Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design
Xi’an University of Posts & Telecommunications（西安邮电大学）；National University of Singapore（新加坡国立大学）；Xi’an Jiaotong University（西安交通大学）；Institute of Systems Medicine, Chinese Academy of Medical Sciences（中国医学科学院系统医学研究院）
